artificial intelligence blockchain building energy management building information modelling data analytics data mining energy efficiency Energy efficient building energy management energy performance indicator flexibility measurement industry 4.0 industry 4.0 maturity assessment Internet of Things knowledge management linked data machine learning manufacturing Ontology ontology engineering ontology population product configuration product lifecycle management production planning and control requirement engineering resource efficient manufacturing smart cities Supply Chain 4.0 sustainability virtual engineering
2023 |
Wicaksono Hendro; Trat, Martin.; Bashyal Atit; Boroukhian Tina; Felder Mine; Ahrens Mischa; Bender Janek; Groß Sebastian; Steiner Daniel; July Christoph; Dorus Christoph; Zoerner Thorsten Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing Journal Article Forthcoming Robotics and Computer-Integrated Manufacturing, Forthcoming. Abstract | BibTeX | Tags: artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning @article{Wicaksono2023, title = {Artificial Intelligence Enabled Dynamic Demand Response System for Maximizing the Use of Green Electricity in Production Processes, Robotics and Computer-Integrated Manufacturing}, author = {Wicaksono, Hendro; Trat, Martin.; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; Dorus, Christoph; Zoerner, Thorsten}, year = {2023}, date = {2023-12-30}, journal = {Robotics and Computer-Integrated Manufacturing}, abstract = {The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.}, keywords = {artificial intelligence, Artificial neural network, machine learning, Ontology, production scheduling, reinforcement learning}, pubstate = {forthcoming}, tppubtype = {article} } The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system's modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities. |
Pidikiti Vamsi Sai; Vijaya, Annas; Fatahi Valilai Omid; Wicaksono Hendro An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry Journal Article Forthcoming Procedia CIRP, Forthcoming. Abstract | BibTeX | Tags: automotive industry, circular economy, Ontology, semantic interoperability @article{Pidikiti2023, title = {An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry}, author = {Pidikiti, Vamsi Sai; Vijaya, Annas; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2023}, date = {2023-10-24}, journal = {Procedia CIRP}, abstract = {Circular economy (CE) focuses on maintaining the value of goods and materials as long as possible, reducing waste and resource usage, and keeping resources within the economy when a product has reached the end of its life. Products and materials have to be utilized many times to produce additional value. In the automotive industry, CE involves processes throughout the value chain comprising multiple dimensions such as energy, materials, lifetime, and utilization. The CE performance of the automotive industry can be measured using key performance indicators (KPIs) from those dimensions. However, since multiple stakeholders are involved throughout the automotive product lifecycle and value chain, calculating KPIs requires data from heterogeneous sources. Thus, a non-uniform understanding of the KPIs among those stakeholders may arise due to a lack of explicit semantic description, including the related assessed CE scenarios, data providers, and data sources. Meanwhile, various sectors have used ontologies to facilitate a common understanding of information structure among systems and organizations. Our paper presents an ontology-based model that enables sharing a common understanding of KPIs used to assess CE performance in the automotive industry. We identify the indicators, the corresponding data requirements, and sources. In this paper, we present the ontology model describing the semantics of data required for the indicators. We also show the deployment model illustrating the implementation of the ontology model in the CE performance assessment phases.}, keywords = {automotive industry, circular economy, Ontology, semantic interoperability}, pubstate = {forthcoming}, tppubtype = {article} } Circular economy (CE) focuses on maintaining the value of goods and materials as long as possible, reducing waste and resource usage, and keeping resources within the economy when a product has reached the end of its life. Products and materials have to be utilized many times to produce additional value. In the automotive industry, CE involves processes throughout the value chain comprising multiple dimensions such as energy, materials, lifetime, and utilization. The CE performance of the automotive industry can be measured using key performance indicators (KPIs) from those dimensions. However, since multiple stakeholders are involved throughout the automotive product lifecycle and value chain, calculating KPIs requires data from heterogeneous sources. Thus, a non-uniform understanding of the KPIs among those stakeholders may arise due to a lack of explicit semantic description, including the related assessed CE scenarios, data providers, and data sources. Meanwhile, various sectors have used ontologies to facilitate a common understanding of information structure among systems and organizations. Our paper presents an ontology-based model that enables sharing a common understanding of KPIs used to assess CE performance in the automotive industry. We identify the indicators, the corresponding data requirements, and sources. In this paper, we present the ontology model describing the semantics of data required for the indicators. We also show the deployment model illustrating the implementation of the ontology model in the CE performance assessment phases. |
Angreani Linda Salma; Vijaya, Annas; Wicaksono Hendro Identifying Essential Driving Factors of Industry 4.0 Maturity Models Using Fuzzy MCDM Methods Journal Article Forthcoming Procedia CIRP, Forthcoming. BibTeX | Tags: Fuzzy TOPSIS, industry 4.0, industry 4.0 maturity assessment, MCDM @article{Angreani2023, title = {Identifying Essential Driving Factors of Industry 4.0 Maturity Models Using Fuzzy MCDM Methods}, author = {Angreani, Linda Salma; Vijaya, Annas; Wicaksono, Hendro}, year = {2023}, date = {2023-10-24}, journal = {Procedia CIRP}, keywords = {Fuzzy TOPSIS, industry 4.0, industry 4.0 maturity assessment, MCDM}, pubstate = {forthcoming}, tppubtype = {article} } |
Sarafanov Egor; Fatahi Valilai, Omid; Wicaksono Hendro Causal analysis of the adoption willingness of artificial intelligence in project management Conference Forthcoming Proceeding of Intelligent Systems Conference (IntelliSys) 2023 , Forthcoming. Abstract | BibTeX | Tags: artificial intelligence, causal analysis, causal inference, causal model, project management, structural equation modelling @conference{Sarafanov2023, title = {Causal analysis of the adoption willingness of artificial intelligence in project management}, author = {Sarafanov, Egor; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2023}, date = {2023-09-07}, booktitle = {Proceeding of Intelligent Systems Conference (IntelliSys) 2023 }, abstract = {Artificial intelligence (AI) technologies have great potential to improve decision-making and automation processes in various sectors, including project management. AI technologies could significantly contribute to overcoming the complexity of project management through process automation, cognitive insight, and engagement. However, the adoption of AI technologies still faces many challenges due to technical, human resource-related, organizational, and legal issues. Our research identified the potential factors that lead to the willingness of people and organizations to adopt AI technologies in project management. This paper proposes a causal model describing multivariate causal relationships between the driving factors and the willingness to adopt AI. The causal model is a set of hypotheses evaluated through a survey and causal analysis using the structural equation modeling (SEM) technique. The analysis focused on six factors influencing the willingness to adopt AI in project management, i.e., performance effectiveness, price, previous experience, feedback, complexity, and complementary technologies. Our research found that the perception of the high effectiveness of AI technologies leading to higher profits and overall the state of the project is the main factor influencing the willingness to adopt AI technologies in project management.}, keywords = {artificial intelligence, causal analysis, causal inference, causal model, project management, structural equation modelling}, pubstate = {forthcoming}, tppubtype = {conference} } Artificial intelligence (AI) technologies have great potential to improve decision-making and automation processes in various sectors, including project management. AI technologies could significantly contribute to overcoming the complexity of project management through process automation, cognitive insight, and engagement. However, the adoption of AI technologies still faces many challenges due to technical, human resource-related, organizational, and legal issues. Our research identified the potential factors that lead to the willingness of people and organizations to adopt AI technologies in project management. This paper proposes a causal model describing multivariate causal relationships between the driving factors and the willingness to adopt AI. The causal model is a set of hypotheses evaluated through a survey and causal analysis using the structural equation modeling (SEM) technique. The analysis focused on six factors influencing the willingness to adopt AI in project management, i.e., performance effectiveness, price, previous experience, feedback, complexity, and complementary technologies. Our research found that the perception of the high effectiveness of AI technologies leading to higher profits and overall the state of the project is the main factor influencing the willingness to adopt AI technologies in project management. |
Sukaridhoto S., Hanifati Fajrianti Haz Al Hafidz Basuki DK.; Budiarti Wicaksono Hendro K ; E D ; A L ; I A ; R P N ; Web-based Extended Reality for Supporting Medical Education Inproceedings Forthcoming Proceeding of Intelligent Systems Conference (IntelliSys) 2023 , Forthcoming. BibTeX | Tags: extended reality, immersive technology @inproceedings{Sukaridhoto2023, title = {Web-based Extended Reality for Supporting Medical Education}, author = {Sukaridhoto, S., Hanifati, K.; Fajrianti, E.D.; Haz, A.L.; Al Hafidz, I.A.; Basuki, DK.; Budiarti, R.P.N.; Wicaksono, Hendro}, year = {2023}, date = {2023-09-07}, booktitle = {Proceeding of Intelligent Systems Conference (IntelliSys) 2023 }, keywords = {extended reality, immersive technology}, pubstate = {forthcoming}, tppubtype = {inproceedings} } |
Sukaridhoto S., Prayudi Al Rasyid Wicaksono Hendro A ; M U H ; Internet of Things Platform as A Service for Building Digital Twins and Blockchain Inproceedings Forthcoming Forthcoming. BibTeX | Tags: digital twins, Internet of Things @inproceedings{Sukaridhoto2023b, title = {Internet of Things Platform as A Service for Building Digital Twins and Blockchain}, author = {Sukaridhoto, S., Prayudi, A.; Al Rasyid, M.U.H.; Wicaksono, Hendro}, year = {2023}, date = {2023-09-07}, keywords = {digital twins, Internet of Things}, pubstate = {forthcoming}, tppubtype = {inproceedings} } |
Vorrink Niclas; Wicaksono, Hendro; Fatahi Valilai Omid Analyzing VR/AR Technology Capabilities for Enhancing the Effectiveness of Learning Processes with Focus on Gamification Inproceedings Forthcoming Proceeding Intelligent Systems Conference (IntelliSys) 2023, Forthcoming. BibTeX | Tags: augmented reality, virtual engineering, virtual reality @inproceedings{Vorrink2023, title = {Analyzing VR/AR Technology Capabilities for Enhancing the Effectiveness of Learning Processes with Focus on Gamification}, author = {Vorrink, Niclas; Wicaksono, Hendro; Fatahi Valilai, Omid }, year = {2023}, date = {2023-09-07}, booktitle = {Proceeding Intelligent Systems Conference (IntelliSys) 2023}, keywords = {augmented reality, virtual engineering, virtual reality}, pubstate = {forthcoming}, tppubtype = {inproceedings} } |
Alan Francisco Caraveo Gomez Llanos Annas Vijaya, Hendro Wicaksono Rating ESG key performance indicators in the airline industry Journal Article Environment, Development and Sustainability, 2023. Abstract | Links | BibTeX | Tags: ESG, MCDM, sustainability @article{Llanos2023, title = {Rating ESG key performance indicators in the airline industry}, author = {Alan Francisco Caraveo Gomez Llanos, Annas Vijaya, Hendro Wicaksono }, url = {https://link.springer.com/article/10.1007/s10668-023-03775-z}, doi = {https://doi.org/10.1007/s10668-023-03775-z}, year = {2023}, date = {2023-08-29}, journal = {Environment, Development and Sustainability}, abstract = {The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives.}, keywords = {ESG, MCDM, sustainability}, pubstate = {published}, tppubtype = {article} } The environmental, social, and governance (ESG) integration finds itself in a transition with rapid developments worldwide, given that the pandemic incentivized companies and investors to focus on other social and governance measures such as ESG ratings. However, the divergence of ratings from the ESG and a lack of transparency lead the companies to report voluntary indicators without standardization. This study aimed to identify the ESG criteria and the most suitable set of key performance indicators (KPIs) in the airline industry after the impact of COVID-19. Furthermore, the second objective was to determine the appropriate weights and ranking of the identified criteria. The multi-criteria decision-making analytical hierarchical process was applied for this purpose. Additionally, the use of intuitionistic variables delivers a comprehensive model for rating the airlines according to their ESG performance. The most relevant criteria found in the study were critical risk management, greenhouse gas emissions, and systemic risk management. Regarding the KPIs, the top-3 weights were the number of flight accidents, jet fuel consumed and sustainable aviation used, and the number of digital transformation initiatives. |
Aikenov Temirlan; Hidayat, Rahmat; Wicaksono Hendro Power consumption and process cost prediction of customized products using explainable AI Inproceedings Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems., 2023. Abstract | Links | BibTeX | Tags: energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI @inproceedings{Aikenov2023, title = {Power consumption and process cost prediction of customized products using explainable AI}, author = {Aikenov, Temirlan; Hidayat, Rahmat; Wicaksono, Hendro}, url = {https://link.springer.com/chapter/10.1007/978-3-031-38165-2_135}, doi = {https://doi.org/10.1007/978-3-031-38165-2_135}, year = {2023}, date = {2023-08-25}, booktitle = {Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.}, abstract = {Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.}, keywords = {energy efficiency, explainable artificial intelligence, machine learning, sustainability, sustainable manuracturing, XAI}, pubstate = {published}, tppubtype = {inproceedings} } Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions. |
Beibit Rauan; Fatahi Valilai, Omid; Wicaksono Hendro Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23), pp. 302–308, Association for Computing Machinery, New York, NY, USA, 2023. Abstract | Links | BibTeX | Tags: COVID-19, machine learning, supply chain management @inproceedings{Beibit2023, title = {Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning}, author = {Beibit, Rauan; Fatahi Valilai, Omid; Wicaksono, Hendro}, url = {https://dl.acm.org/doi/10.1145/3587889.3588215}, doi = {10.1145/3587889.3588215}, year = {2023}, date = {2023-06-09}, booktitle = {Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23)}, pages = {302–308}, publisher = {Association for Computing Machinery, New York, NY, USA}, abstract = {The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors.}, keywords = {COVID-19, machine learning, supply chain management}, pubstate = {published}, tppubtype = {inproceedings} } The COVID-19 pandemic impacted different industrial sectors. It causes semiconductor shortage and, subsequently, on the industries downstream, such as the automotive industry. It is because of factory shutdown, increasing consumer electronic demands due to working from home, shifted focus of companies to consumer electronics, and limited logistic capacity. This research aims to analyze the influencing factors and estimate the extent of the impact of COVID-19 on the semiconductor and automotive industry in Europe using machine learning. We developed five regression models to predict the semiconductor sales and number of new passenger car registrations that reflect the development of sales in the automotive industry. Our research revealed that random forest regression is the best machine learning model for analyzing the relationship between COVID-19, semiconductor sales, and passenger car registrations. However, overall, our research found that the COVID-19 pandemic is not the only factor that impacts the semiconductor shortage in the automotive industry. The geopolitical landscape and the world’s reliance on Chinese exports are also essential influencing factors in many supply chains, including in the semiconductor and automotive sectors. |
Krstevski Stefan; Fatahi Valilai, Omid; Wicaksono Hendro In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23), pp. 98–106, Association for Computing Machinery, New York, NY, USA, 2023. Abstract | Links | BibTeX | Tags: energy efficiency, manufacturing, operation research, production planning and control, production scheduling, sustainability @inproceedings{Krstevski2023, title = {Integrating Real-Time Dynamic Electricity Price Forecast into Job Shop Production Scheduling Model with Multiple Machine Environments}, author = {Krstevski, Stefan; Fatahi Valilai, Omid; Wicaksono, Hendro }, url = {https://doi.org/10.1145/3587889.3587905}, doi = {10.1145/3587889.3587905}, year = {2023}, date = {2023-06-09}, booktitle = {In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (ICIEAEU '23)}, pages = {98–106}, publisher = {Association for Computing Machinery, New York, NY, USA}, abstract = {One of the challenges in the transition towards green electricity is the intermittence of power generated by renewable sources. Thus, power consumers, including the manufacturing industry, must adapt their activities and processes to green electricity supply. Real-time dynamic pricing is an approach to encourage electricity consumers to change their consumption patterns by lowering prices when the availability of green electricity in the grid is high. Due to the introduction of real-time electricity pricing, manufacturing companies must adapt their production planning by integrating dynamic price information into their production scheduling. Our research focuses on extending the basic production scheduling mathematical model by introducing real-time power pricing in the model. The prices are built based on the current proportion of green electricity in the grid represented in the green electricity index (GEI) with one-hour intervals. This paper also illustrates a scenario of how to use the model. Our future research will further extend the model addressing the flexibility of manufacturing shop floors (e.g. adding buffer, retooling, and setup time) and validate the model in two small and medium manufacturing enterprises.}, keywords = {energy efficiency, manufacturing, operation research, production planning and control, production scheduling, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } One of the challenges in the transition towards green electricity is the intermittence of power generated by renewable sources. Thus, power consumers, including the manufacturing industry, must adapt their activities and processes to green electricity supply. Real-time dynamic pricing is an approach to encourage electricity consumers to change their consumption patterns by lowering prices when the availability of green electricity in the grid is high. Due to the introduction of real-time electricity pricing, manufacturing companies must adapt their production planning by integrating dynamic price information into their production scheduling. Our research focuses on extending the basic production scheduling mathematical model by introducing real-time power pricing in the model. The prices are built based on the current proportion of green electricity in the grid represented in the green electricity index (GEI) with one-hour intervals. This paper also illustrates a scenario of how to use the model. Our future research will further extend the model addressing the flexibility of manufacturing shop floors (e.g. adding buffer, retooling, and setup time) and validate the model in two small and medium manufacturing enterprises. |
Agung Teguh Wibowo Almais Adi Susilo, Agus Naba Moechammad Sarosa Cahyo Crysdian Imam Tazi Mokhamad Amin Hariyadi Muhammad Aziz Muslim Puspa Miladin Nuraida Safitri Abdul Basid Yunifa Miftachul Arif Mohammad Singgih Purwanto Diyan Parwatiningtyas Hendro Wicaksono Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters Journal Article IEEE Access, 11 , pp. 74590 - 74601, 2023. Abstract | Links | BibTeX | Tags: artificial intelligence, machine learning, PCA @article{Almais2023, title = {Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters}, author = {Agung Teguh Wibowo Almais, Adi Susilo, Agus Naba, Moechammad Sarosa, Cahyo Crysdian, Imam Tazi, Mokhamad Amin Hariyadi, Muhammad Aziz Muslim, Puspa Miladin Nuraida Safitri Abdul Basid, Yunifa Miftachul Arif, Mohammad Singgih Purwanto, Diyan Parwatiningtyas, Hendro Wicaksono}, url = {https://ieeexplore.ieee.org/abstract/document/10123944}, doi = {10.1109/ACCESS.2023.3275852}, year = {2023}, date = {2023-05-12}, journal = {IEEE Access}, volume = {11}, pages = {74590 - 74601}, abstract = {Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $\text{n} < 0$ ), cluster 2 ( $0\le \text{n} < 2$ ), and cluster 3 ( $\text{n}\ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0\le \text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $\text{n}\ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing.}, keywords = {artificial intelligence, machine learning, PCA}, pubstate = {published}, tppubtype = {article} } Post-disaster sector damage data is data that has features or criteria in each case the level of damage to the post-natural disaster sector data. These criteria data are building conditions, building structures, building physicals, building functions, and other supporting conditions. Data on the level of damage to the post-natural disaster sector used in this study amounted to 216 data, each of which has 5 criteria for damage to the post-natural disaster sector. Then PCA is used to look for labels in each data. The results of these labels will be used to cluster data based on the value scale of the results of data normalization in the PCA process. In the data normalization process at PCA, the data is divided into 2 components, namely PC1 and PC2. Each component has a variance ratio and eigenvalue generated in the PCA process. For PC1 it has a variance ratio of 85.17% and an eigenvalue of 4.28%, while PC2 has a variance ratio of 9.36% and an eigenvalue of 0.47%. The results of data normalization are then made into a 2-dimensional graph to see the data visualization of the results of each main component (PC). The result is that there is 3 data cluster using a value scale based on the PCA results chart. The coordinate value (n) of each cluster is cluster 1 ( $text{n} < 0$ ), cluster 2 ( $0le text{n} < 2$ ), and cluster 3 ( $text{n}ge 2$ ). To test these 3 groups of data, it is necessary to conduct trials by comparing the original target data, there are two experiments, namely testing the PC1 results based on the original target data, and the PC2 results based on the original target data. The result is that there are 2 updates, the first is that the distribution of PC1 data is very good when comparing the distribution of data with PC2 in grouping data, because the eigenvalue of PC1 is greater than that of PC2. While second, the results of testing the PC1 data with the original target data produce good data grouping, because the original target data which has a value of 1 (slightly damaged) occupies the coordinates of group 1 (n < 0), the original target data which has a value of 2 (moderately damaged) occupies group 2 coordinates ( $0le text{n} < 2$ ), and for the original target data the value 3 (heavily damaged) occupies group 3 coordinates ( $text{n}ge 2$ ). Therefore, it can be concluded that PCA, which so far has been used by many studies as feature reduction, this study uses PCA for labeling unsupervised data so that it has appropriate data labels for further processing. |
Sukoco Badri Munir; Putra, Rizky Ananda.; Muqaffi Humam Nur; Lutfian Muhammad Vinka; Wicaksono Hendro Comparative Study of ASEAN Research Productivity Journal Article SAGE Open, 13 (1), pp. 21582440221145157, 2023. Abstract | Links | BibTeX | Tags: data analytics, research @article{Sukoco2023, title = {Comparative Study of ASEAN Research Productivity}, author = {Sukoco, Badri Munir; Putra, Rizky Ananda.; Muqaffi, Humam Nur; Lutfian, Muhammad Vinka; Wicaksono, Hendro}, url = {https://journals.sagepub.com/doi/full/10.1177/21582440221145157}, doi = { https://doi.org/10.1177/21582440221145157}, year = {2023}, date = {2023-01-03}, journal = {SAGE Open}, volume = {13}, number = {1}, pages = {21582440221145157}, abstract = {Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering & Technology and Life Sciences & Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization.}, keywords = {data analytics, research}, pubstate = {published}, tppubtype = {article} } Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering & Technology and Life Sciences & Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization. |
2022 |
Istiqomah Silvi; Sutopo, Wahyudi; Hisjam Muhammad; Wicaksono Hendro Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta Journal Article World Electric Vehicle Journal, 13 (12), pp. 232, 2022. Abstract | Links | BibTeX | Tags: mixed integer linear programming, operation research @article{Istiqomah2022, title = {Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta}, author = {Istiqomah, Silvi; Sutopo, Wahyudi; Hisjam, Muhammad; Wicaksono, Hendro}, doi = {https://doi.org/10.3390/wevj13120232}, year = {2022}, date = {2022-12-05}, journal = {World Electric Vehicle Journal}, volume = {13}, number = {12}, pages = {232}, abstract = {first_pagesettingsOrder Article Reprints Open AccessArticle Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta by Silvi Istiqomah 1,2,Wahyudi Sutopo 1,3,*ORCID,Muhammad Hisjam 1ORCID andHendro Wicaksono 4 1 Master Program of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami, No. 36A, Surakarta 57126, Indonesia 2 Department of Industrial Engineering, Institut Teknologi Telkom Surabaya, Jl. Ketintang No. 156, Ketintang, Kec. Gayungan, Surabaya 60231, Indonesia 3 Centre of Excellence for Electrical Energy Storage Technology, Universitas Sebelas Maret, Surakarta, Jl. Slamet Riyadi, No. 435, Purwosari, Laweyan, Surakarta 57146, Indonesia 4 Mathematics and Logistics, Jacobs University Bremen German, Campus Ring 1, 28759 Bremen, Germany * Author to whom correspondence should be addressed. World Electr. Veh. J. 2022, 13(12), 232; https://doi.org/10.3390/wevj13120232 Received: 6 November 2022 / Revised: 29 November 2022 / Accepted: 30 November 2022 / Published: 5 December 2022 Download Browse Figures Versions Notes Abstract Many benefits follow from the use of Electric Vehicles (EVs) to replace fossil fuel-based vehicles (FVs), i.e., improved transportation energy efficiency, reduced carbon and noise emissions, and the mitigation of tailpipe emissions. However, replacing conventional FVs with EVs requires the establishment of a suitable charging infrastructure representing a commonplace detail that blends into the landscape and is available in various locations. This research focuses on the infrastructure of Electric Motorcycles (EM), constituting a relatively dense network of charging stations (CS), which is an essential factor in accelerating the commercialization of EM in Indonesia. In this case study, we propose a Charging Infrastructure Optimization approach for placing charging stations to meet the demand posed by motorcycles. This study uses motorcycle user data as the initiation data for electric motorcycle users. The selection of charging station development points uses the calculation methods of the centrality index and scalogram, which describe the density of community activities. After the charging station’s construction point is obtained, the point is validated with the optimization model that has been designed with respect to the Maximal Covering Location Problem. We also analyze the benefits and costs of constructing this charging station to determine its feasibility.}, keywords = {mixed integer linear programming, operation research}, pubstate = {published}, tppubtype = {article} } first_pagesettingsOrder Article Reprints Open AccessArticle Optimizing Electric Motorcycle-Charging Station Locations for Easy Accessibility and Public Benefit: A Case Study in Surakarta by Silvi Istiqomah 1,2,Wahyudi Sutopo 1,3,*ORCID,Muhammad Hisjam 1ORCID andHendro Wicaksono 4 1 Master Program of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami, No. 36A, Surakarta 57126, Indonesia 2 Department of Industrial Engineering, Institut Teknologi Telkom Surabaya, Jl. Ketintang No. 156, Ketintang, Kec. Gayungan, Surabaya 60231, Indonesia 3 Centre of Excellence for Electrical Energy Storage Technology, Universitas Sebelas Maret, Surakarta, Jl. Slamet Riyadi, No. 435, Purwosari, Laweyan, Surakarta 57146, Indonesia 4 Mathematics and Logistics, Jacobs University Bremen German, Campus Ring 1, 28759 Bremen, Germany * Author to whom correspondence should be addressed. World Electr. Veh. J. 2022, 13(12), 232; https://doi.org/10.3390/wevj13120232 Received: 6 November 2022 / Revised: 29 November 2022 / Accepted: 30 November 2022 / Published: 5 December 2022 Download Browse Figures Versions Notes Abstract Many benefits follow from the use of Electric Vehicles (EVs) to replace fossil fuel-based vehicles (FVs), i.e., improved transportation energy efficiency, reduced carbon and noise emissions, and the mitigation of tailpipe emissions. However, replacing conventional FVs with EVs requires the establishment of a suitable charging infrastructure representing a commonplace detail that blends into the landscape and is available in various locations. This research focuses on the infrastructure of Electric Motorcycles (EM), constituting a relatively dense network of charging stations (CS), which is an essential factor in accelerating the commercialization of EM in Indonesia. In this case study, we propose a Charging Infrastructure Optimization approach for placing charging stations to meet the demand posed by motorcycles. This study uses motorcycle user data as the initiation data for electric motorcycle users. The selection of charging station development points uses the calculation methods of the centrality index and scalogram, which describe the density of community activities. After the charging station’s construction point is obtained, the point is validated with the optimization model that has been designed with respect to the Maximal Covering Location Problem. We also analyze the benefits and costs of constructing this charging station to determine its feasibility. |
Ganesan Santhosh; Wicaksono, Hendro; Fatahi Valilai Omid Enhancing Vendor Managed Inventory with the Application of Blockchain Technology Inproceedings Advances in System-Integrated Intelligence, pp. 262-275, Springer International Publishing, 2022, ISBN: 978-3-031-16281-7. Abstract | Links | BibTeX | Tags: blockchain, Supply Chain 4.0, supply chain management @inproceedings{Ganesan2022, title = {Enhancing Vendor Managed Inventory with the Application of Blockchain Technology}, author = {Ganesan, Santhosh; Wicaksono, Hendro; Fatahi Valilai, Omid }, url = {https://link.springer.com/chapter/10.1007/978-3-031-16281-7_26}, doi = {https://doi.org/10.1007/978-3-031-16281-7_26}, isbn = {978-3-031-16281-7}, year = {2022}, date = {2022-09-04}, booktitle = { Advances in System-Integrated Intelligence}, pages = {262-275}, publisher = {Springer International Publishing}, abstract = {As a result of globalization, supply chain networks have grown in complexity and size, posing several issues and chances for development. Inventory optimization and replenishment policy adjustments have a substantial impact on supply chain operating performance and profitability. Vendor Managed Inventory (VMI) is a mutually advantageous agreement between a supplier and a customer in which the supplier oversees inventory and replenishment decisions based on the inventory status of the customer. VMI operations encounter major hurdles in today’s supply chains, including trust, data integrity, transparency, and traceability for multiple supplier and customer interactions. Blockchain technology is a distributed ledger that ensures that data is exchanged in a transparent, safe, and secure manner across supply chain stakeholders. The advantages of adopting blockchain technology for VMI operations in a supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. This paper discusses a blockchain-based approach to enhance the VMI supply chain operations. It proposes a generic framework to enable the suppliers and customers order matching in a decentralized mode while fulfilling the trust in terms of managing the data accessibility among the stakeholders as an important prerequisite for VMI establishment. A case study is designed to compare the traditional and blockchain solutions.}, keywords = {blockchain, Supply Chain 4.0, supply chain management}, pubstate = {published}, tppubtype = {inproceedings} } As a result of globalization, supply chain networks have grown in complexity and size, posing several issues and chances for development. Inventory optimization and replenishment policy adjustments have a substantial impact on supply chain operating performance and profitability. Vendor Managed Inventory (VMI) is a mutually advantageous agreement between a supplier and a customer in which the supplier oversees inventory and replenishment decisions based on the inventory status of the customer. VMI operations encounter major hurdles in today’s supply chains, including trust, data integrity, transparency, and traceability for multiple supplier and customer interactions. Blockchain technology is a distributed ledger that ensures that data is exchanged in a transparent, safe, and secure manner across supply chain stakeholders. The advantages of adopting blockchain technology for VMI operations in a supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. This paper discusses a blockchain-based approach to enhance the VMI supply chain operations. It proposes a generic framework to enable the suppliers and customers order matching in a decentralized mode while fulfilling the trust in terms of managing the data accessibility among the stakeholders as an important prerequisite for VMI establishment. A case study is designed to compare the traditional and blockchain solutions. |
Lobo Carol Riona;, Wicaksono Hendro; Fatahi Valilai Omid Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives Journal Article IFAC-PapersOnLine, 55 (10), pp. 3304-3309, 2022. Abstract | Links | BibTeX | Tags: blockchain, logistics 4.0, sustainability @article{Lobo2022, title = {Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives}, author = {Lobo, Carol Riona;, Wicaksono, Hendro; Fatahi Valilai, Omid}, url = {https://www.sciencedirect.com/science/article/pii/S2405896322021334}, doi = {https://doi.org/10.1016/j.ifacol.2022.10.123}, year = {2022}, date = {2022-06-22}, journal = {IFAC-PapersOnLine}, volume = {55}, number = {10}, pages = {3304-3309}, abstract = {The advancement in technology, such as, Smart Logistics, IoT, RFID, sensors, and 5G, resulted in the evolution of Industry 4.0 that has started gaining a lot of popularity among different sectors like last mile delivery. This is important as the rising demand for such technology enabled platforms has been found to be necessary for fulfilling the opt for e-commerce services to support the retail outlets. The literature shows that to relax the pressure on the last mile sector, blockchain technology can be an effective solution both to protect the firm financial aspects and sustainability requirements. To ensure efficiency in the system and success in the implementation of blockchain technology into the last mile delivery sector, it is essential to study the various factors and capabilities of blockchain to handle the existing problems and requirements to analyze the efficiency of this integration. The focus areas of this paper are mainly to identify the impact of applying blockchain technology to support the last mile delivery of goods. The impacted areas focus mainly on the efficiency of the process and its leverage on the costs, both administrative and operational, and level of sustainability achieved. The proposed platform has enabled the enhancement of the integration of blockchain into the last mile delivery. The proposed smart contract system is designed to efficiently assign the orders from the demander to the respective fleet providers with the help of miners. This assignment is made possible by considering the various aspects that have been stored into the system, namely geographical location, the proximity to the destination of delivery along the route of delivery, size of the parcels, and capacity of the fleet. Keywords: Last mile delivery; Blockchain Technology; Smart Contract; Transparency; Sustainability }, keywords = {blockchain, logistics 4.0, sustainability}, pubstate = {published}, tppubtype = {article} } The advancement in technology, such as, Smart Logistics, IoT, RFID, sensors, and 5G, resulted in the evolution of Industry 4.0 that has started gaining a lot of popularity among different sectors like last mile delivery. This is important as the rising demand for such technology enabled platforms has been found to be necessary for fulfilling the opt for e-commerce services to support the retail outlets. The literature shows that to relax the pressure on the last mile sector, blockchain technology can be an effective solution both to protect the firm financial aspects and sustainability requirements. To ensure efficiency in the system and success in the implementation of blockchain technology into the last mile delivery sector, it is essential to study the various factors and capabilities of blockchain to handle the existing problems and requirements to analyze the efficiency of this integration. The focus areas of this paper are mainly to identify the impact of applying blockchain technology to support the last mile delivery of goods. The impacted areas focus mainly on the efficiency of the process and its leverage on the costs, both administrative and operational, and level of sustainability achieved. The proposed platform has enabled the enhancement of the integration of blockchain into the last mile delivery. The proposed smart contract system is designed to efficiently assign the orders from the demander to the respective fleet providers with the help of miners. This assignment is made possible by considering the various aspects that have been stored into the system, namely geographical location, the proximity to the destination of delivery along the route of delivery, size of the parcels, and capacity of the fleet. Keywords: Last mile delivery; Blockchain Technology; Smart Contract; Transparency; Sustainability |
Navendan Karthikeyan; Wicaksono, Hendro; Fatahi Valilai Omid Enhancement of Crowd Logistics Model in an E-Commerce Scenario Using Blockchain-Based Decentralized Application Inproceedings Freitag M., Kinra Kotzab Megow A H N (Ed.): International Conference on Dynamics in Logistics, pp. 26-37, Springer, Cham, 2022, ISBN: 978-3-031-05359-7. Abstract | Links | BibTeX | Tags: blockchain, Supply Chain 4.0, supply chain management @inproceedings{Navendan2022, title = {Enhancement of Crowd Logistics Model in an E-Commerce Scenario Using Blockchain-Based Decentralized Application}, author = {Navendan, Karthikeyan; Wicaksono, Hendro; Fatahi Valilai, Omid }, editor = {Freitag, M., Kinra, A., Kotzab, H., Megow, N. }, url = {https://link.springer.com/chapter/10.1007/978-3-031-05359-7_3}, doi = {https://doi.org/10.1007/978-3-031-05359-7_3}, isbn = {978-3-031-05359-7}, year = {2022}, date = {2022-05-05}, booktitle = {International Conference on Dynamics in Logistics}, pages = {26-37}, publisher = {Springer, Cham}, abstract = {Globalization and developments in technology have contributed to the growth of numerous industries around the globe which are creating major impacts on today's supply chains. The supply chain has drastically changed under the open-ended influence of globalization. The need for different types of mobilities is increasing due to urbanization and population growth, rapid development in the E-commerce industry, and the growing expectation of customers. Crowd logistics is one of these techniques that is gaining rapid attention in the logistics industries and many start-ups have started using this method in their business models. This paper has investigated the crowd logistics and the challenges like user trust, data safety and security, security of the financial transactions for both the customer and the crowd and tracking service quality. Using the Blockchain technology, an e-commerce crowd logistics conceptual model is proposed. Moreover, through different scenarios the capabilities of the proposed model besides the detailed flow of the business processes have been discussed. The conceptual model of a Blockchain-based crowd logistics has used the functionalities of Blockchain such as the smart contracts and the DApps and especially has increased the flexibility of the crowd logistics system. }, keywords = {blockchain, Supply Chain 4.0, supply chain management}, pubstate = {published}, tppubtype = {inproceedings} } Globalization and developments in technology have contributed to the growth of numerous industries around the globe which are creating major impacts on today's supply chains. The supply chain has drastically changed under the open-ended influence of globalization. The need for different types of mobilities is increasing due to urbanization and population growth, rapid development in the E-commerce industry, and the growing expectation of customers. Crowd logistics is one of these techniques that is gaining rapid attention in the logistics industries and many start-ups have started using this method in their business models. This paper has investigated the crowd logistics and the challenges like user trust, data safety and security, security of the financial transactions for both the customer and the crowd and tracking service quality. Using the Blockchain technology, an e-commerce crowd logistics conceptual model is proposed. Moreover, through different scenarios the capabilities of the proposed model besides the detailed flow of the business processes have been discussed. The conceptual model of a Blockchain-based crowd logistics has used the functionalities of Blockchain such as the smart contracts and the DApps and especially has increased the flexibility of the crowd logistics system. |
Khaturia Roshaali; Wicaksono Hendro; Fatahi Valilai, Omid SRP: A Sustainable Dynamic Ridesharing Platform Utilizing Blockchain Technology Inproceedings International Conference on Dynamics in Logistics, 2022, ISBN: 978-3-031-05359-7. Abstract | Links | BibTeX | Tags: blockchain, sustainability @inproceedings{Khaturia2022, title = {SRP: A Sustainable Dynamic Ridesharing Platform Utilizing Blockchain Technology}, author = {Khaturia, Roshaali; Wicaksono Hendro; Fatahi Valilai, Omid }, url = {https://link.springer.com/chapter/10.1007/978-3-031-05359-7_24}, doi = {https://doi.org/10.1007/978-3-031-05359-7_24}, isbn = {978-3-031-05359-7}, year = {2022}, date = {2022-05-05}, booktitle = { International Conference on Dynamics in Logistics}, abstract = {With the growing carbon-di-oxide (CO2) emissions and road vehicles being responsible for almost 75% of the emissions, it is imperative to put in efforts to reduce CO2, especially in the transportation sector. Ridesharing services enable users to use cars more wisely by filling the vacant spaces with passengers having similar itineraries and time schedules. However, most of the ridesharing services are dependent on a third party for the interaction between the riders and drivers. Relying on a third party and central server can turn out to be expensive since a commission is charged by the third-party; risky since it is more prone to going down and malicious attacks; might not lead to the most appropriate matches; and in case the security of the service provider is not protected and jeopardized, there are high chances of the service being disturbed and the data of the users being disclosed or tampered with. This paper has proposed SRP-A sustainable ridesharing platform that replaces the third party/central server by Blockchain technology. This platform makes use of Blockchain’s capabilities such as consensus mechanism (Proof of Stake); smart contracts; and solvers, making the entire system more secure and less prone to attacks along with tackling the issue of excessive emissions of CO2 in the environment. }, keywords = {blockchain, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } With the growing carbon-di-oxide (CO2) emissions and road vehicles being responsible for almost 75% of the emissions, it is imperative to put in efforts to reduce CO2, especially in the transportation sector. Ridesharing services enable users to use cars more wisely by filling the vacant spaces with passengers having similar itineraries and time schedules. However, most of the ridesharing services are dependent on a third party for the interaction between the riders and drivers. Relying on a third party and central server can turn out to be expensive since a commission is charged by the third-party; risky since it is more prone to going down and malicious attacks; might not lead to the most appropriate matches; and in case the security of the service provider is not protected and jeopardized, there are high chances of the service being disturbed and the data of the users being disclosed or tampered with. This paper has proposed SRP-A sustainable ridesharing platform that replaces the third party/central server by Blockchain technology. This platform makes use of Blockchain’s capabilities such as consensus mechanism (Proof of Stake); smart contracts; and solvers, making the entire system more secure and less prone to attacks along with tackling the issue of excessive emissions of CO2 in the environment. |
Wicaksono Hendro; Yuce, Baris; McGlinn Kris; Calli Ozum Smart Cities and Buildings Book Chapter Chapter Smart cities and buildings, pp. 239-263, CRC Press, 1st Edition, 2022, ISBN: 9781003204381. Abstract | Links | BibTeX | Tags: energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability @inbook{Wicaksono2022b, title = {Smart Cities and Buildings}, author = {Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum}, url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781003204381-13/smart-cities-buildings-hendro-wicaksono-baris-yuce-kris-mcglinn-ozum-calli}, isbn = {9781003204381}, year = {2022}, date = {2022-05-01}, pages = {239-263}, publisher = {CRC Press}, edition = {1st Edition}, chapter = {Smart cities and buildings}, abstract = {Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. }, keywords = {energy efficiency, machine learning, Ontology, smart cities, smart energy, sustainability}, pubstate = {published}, tppubtype = {inbook} } Smart buildings function within the wider context of the smart city, which itself must function within the wider energy and transport (smart) grids. It is essential therefore that smart buildings be integrated into this wider context. This requires intelligent approaches for managing and coordinating the diverse range of processes and technologies involved and a move towards a “digital infrastructure” which can transform how these smart environments operate and can be monitored but, more importantly, can circumvent the constraints of physical infrastructure through the capacity of data centres or the capacity of available communication pipes. This chapter explores the concept of the smart city, and the role that smart buildings, smart energy grids and smart transportation takes within, with a particular emphasis on the state of art with respect to the integration of data across these different domains, from the micro to the macro, from building sensors to smart grids. It explores different data analytics approaches, and it does this with reference to specific use cases, focusing on techniques in the main application areas along with relevant implemented examples while highlighting some of the key challenges currently faced and outlining future pathways for the sector. |
2021 |
Wicaksono Hendro; Boroukhian, Tina; Bashyal Atit A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning Book Chapter Freitag, Michael ; Kotzab, Herbert ; Megow, Nicole (Ed.): pp. 155-181, Springer, 2021, ISBN: 978-3-030-88662-2. Abstract | Links | BibTeX | Tags: artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability @inbook{Wicaksono2021, title = {A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning}, author = {Wicaksono, Hendro; Boroukhian, Tina; Bashyal, Atit }, editor = {Freitag, Michael and Kotzab, Herbert and Megow, Nicole}, doi = {10.1007/978-3-030-88662-2_8}, isbn = {978-3-030-88662-2}, year = {2021}, date = {2021-12-31}, pages = {155-181}, publisher = {Springer}, abstract = {The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.}, keywords = {artificial intelligence, causal analysis, causal inference, causal model, energy transition, linked data, machine learning, Ontology, project management, structural equation modelling, sustainability}, pubstate = {published}, tppubtype = {inbook} } The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets. |
Ahmadi Elham; Fatahi Valilai, Omid; Wicaksono Hendro Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach Inproceedings 2021. BibTeX | Tags: data analytics, machine learning, sentiment analysis, sustainability @inproceedings{Ahmadi2020, title = {Extending the Last Mile Delivery Routing Problem for Enhancing Sustainability by Drones Using a Sentiment Analysis Approach}, author = {Ahmadi, Elham; Fatahi Valilai, Omid; Wicaksono, Hendro}, year = {2021}, date = {2021-12-14}, keywords = {data analytics, machine learning, sentiment analysis, sustainability}, pubstate = {published}, tppubtype = {inproceedings} } |
Farooq Yousuf; Wicaksono, Hendro Advancing on the analysis of causes and consequences of green skepticism Journal Article Journal of Cleaner Production, 320 , pp. 128927, 2021. Abstract | Links | BibTeX | Tags: data analytics, green skepticism, structural equation modelling, sustainability @article{Farooq2020, title = {Advancing on the analysis of causes and consequences of green skepticism}, author = {Farooq, Yousuf; Wicaksono, Hendro}, doi = {https://doi.org/10.1016/j.jclepro.2021.128927}, year = {2021}, date = {2021-10-20}, journal = {Journal of Cleaner Production}, volume = {320}, pages = {128927}, abstract = {With the increasing trend toward sustainable purchasing, companies invest vast sums of money advertising their sustainability. Yet there are also companies doing the exact opposite for fear of consumer skepticism toward sustainability claims. Consumer skepticism can have adverse effects on company image and performance. Therefore, for the success of a company's sustainability campaign, it is essential that they are familiar with the factors resulting in consumer skepticism. This research has investigated these factors. Through a survey-based approach and analysis using structural equation modeling, it has been found that a main cause of consumer skepticism is previous incidents of greenwashing. Furthermore, consumers are more skeptical of large companies than smaller companies. The research also indicates that consumer skepticism towards a company is industry-specific, with the oil industry being the least trusted. The effect of demographics was also studied, finding that women are more skeptical. Contrary to previous literature, collectivist cultures were found to be more skeptical than individualistic cultures. This research has also explored consumer perspectives towards silent sustainability, finding that highly skeptical consumers prefer companies to limit their sustainability advertisements. Companies silent about their sustainability invoke less consumer skepticism than those advertising sustainability. This research has filled major research gaps in the field of consumer skepticism and silent sustainability and carries important implications for companies advertising in today's market, as well as for policy makers.}, keywords = {data analytics, green skepticism, structural equation modelling, sustainability}, pubstate = {published}, tppubtype = {article} } With the increasing trend toward sustainable purchasing, companies invest vast sums of money advertising their sustainability. Yet there are also companies doing the exact opposite for fear of consumer skepticism toward sustainability claims. Consumer skepticism can have adverse effects on company image and performance. Therefore, for the success of a company's sustainability campaign, it is essential that they are familiar with the factors resulting in consumer skepticism. This research has investigated these factors. Through a survey-based approach and analysis using structural equation modeling, it has been found that a main cause of consumer skepticism is previous incidents of greenwashing. Furthermore, consumers are more skeptical of large companies than smaller companies. The research also indicates that consumer skepticism towards a company is industry-specific, with the oil industry being the least trusted. The effect of demographics was also studied, finding that women are more skeptical. Contrary to previous literature, collectivist cultures were found to be more skeptical than individualistic cultures. This research has also explored consumer perspectives towards silent sustainability, finding that highly skeptical consumers prefer companies to limit their sustainability advertisements. Companies silent about their sustainability invoke less consumer skepticism than those advertising sustainability. This research has filled major research gaps in the field of consumer skepticism and silent sustainability and carries important implications for companies advertising in today's market, as well as for policy makers. |
Wicaksono, Hendro Accelerating Energy Transition to Green Electricity through Artificial Intelligence Presentation 24.08.2021. Abstract | Links | BibTeX | Tags: artificial intelligence, data analytics, energy transition, machine learning @misc{Wicaksono2021c, title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence}, author = {Wicaksono, Hendro }, doi = {10.31219/osf.io/tcrkh}, year = {2021}, date = {2021-08-24}, abstract = {The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform.}, keywords = {artificial intelligence, data analytics, energy transition, machine learning}, pubstate = {published}, tppubtype = {presentation} } The presentation focuses on the role of artificial intelligence in accelerating the transition to green electricity in Germany. It discusses the challenges in the transition towards green electricity in Germany and the role of digitalization through smart metering. One of the methods to adopt and disseminate the use of green electricity is demand response. The presentation explains the definition of demand response concept and gives an example of projects that applies neural network to forecast power generation and consumption to enable calculation of dynamic electricity price. Finally, the presentation explores the adoption of green electricity in broader contexts, e.g., cities and districts, through a data-driven smart energy platform. |
Fritz Simon; Srikanthan, Vethiga; Arbai Ryan; Sun Chenwei; Ovtcharova Jivka; Wicaksono Hendro Automatic Information Extraction from Text-Based Requirements Journal Article International Journal of Knowledge Engineering, 7 (1), 2021, ISSN: 2382-6185. Abstract | Links | BibTeX | Tags: context-sensitive assistance, NLP, Requirements engineering @article{Fritz2021, title = {Automatic Information Extraction from Text-Based Requirements}, author = {Fritz, Simon; Srikanthan, Vethiga; Arbai, Ryan; Sun, Chenwei; Ovtcharova, Jivka; Wicaksono, Hendro }, doi = {10.18178/ijke.2021.7.1.134}, issn = {2382-6185}, year = {2021}, date = {2021-06-01}, journal = {International Journal of Knowledge Engineering}, volume = {7}, number = {1}, abstract = {Requirements form the legal basis for many development pro-jects. They are usually exchanged between customer and supplier in the form of product and requirements specifications and re-quire a subsequent integration effort into the corresponding requirements management solutions. Especially for small and medium-sized enterprises (SME), which mainly use office solutions for the management of requirements, this involves a very high integration effort, which is why this is usually only partially managed or not managed at all. Software solutions available on the market already offer support, but they are too expensive or complex, especially for small companies. The project DAM4KMU, funded by German Federal Ministry for Education and Research (BMBF), addresses this challenge and by enabling SMEs from Germany to integrate requirement documents automatically into existing requirement structures with the help of NLP-based techniques. For this purpose, the documents to be processed are divided into semantic roles, which can then be transferred into a semantic data structure. This in turn enables an automatic linking of the requirements and system components, which reduces the manual effort and avoids possible errors.}, keywords = {context-sensitive assistance, NLP, Requirements engineering}, pubstate = {published}, tppubtype = {article} } Requirements form the legal basis for many development pro-jects. They are usually exchanged between customer and supplier in the form of product and requirements specifications and re-quire a subsequent integration effort into the corresponding requirements management solutions. Especially for small and medium-sized enterprises (SME), which mainly use office solutions for the management of requirements, this involves a very high integration effort, which is why this is usually only partially managed or not managed at all. Software solutions available on the market already offer support, but they are too expensive or complex, especially for small companies. The project DAM4KMU, funded by German Federal Ministry for Education and Research (BMBF), addresses this challenge and by enabling SMEs from Germany to integrate requirement documents automatically into existing requirement structures with the help of NLP-based techniques. For this purpose, the documents to be processed are divided into semantic roles, which can then be transferred into a semantic data structure. This in turn enables an automatic linking of the requirements and system components, which reduces the manual effort and avoids possible errors. |
2020 |
Falah Muhammad Fajrul; Sukaridhoto, Sritrusta; Al Rasyid Muhammad Udin Harun; Wicaksono Hendro Design of Virtual Engineering and Digital Twin Platform as Implementation of Cyber-Physical Systems Journal Article Procedia Manufacturing, 52 , pp. 331-336, 2020. Abstract | Links | BibTeX | Tags: Cyber-physical systems, digital twins, virtual engineering @article{HendroWicaksono2020, title = {Design of Virtual Engineering and Digital Twin Platform as Implementation of Cyber-Physical Systems}, author = {Falah, Muhammad Fajrul; Sukaridhoto, Sritrusta; Al Rasyid, Muhammad Udin Harun; Wicaksono, Hendro}, url = {https://doi.org/10.1016/j.promfg.2020.11.055}, doi = {10.1016/j.promfg.2020.11.055}, year = {2020}, date = {2020-12-31}, journal = {Procedia Manufacturing}, volume = {52}, pages = {331-336}, abstract = {Many industries in Indonesia face several challenges to adopting new technologies of industry 4.0, especially for Digital Twin (DT) and Virtual Engineering (VE), which are integrated with Cyber-Physical Systems (CPS). Lack of human resource and high costs are still significant challenges to developing DT and VE. This preliminary research aims to introduce the initial design of a platform to create Virtual Engineering applications that combine Digital Twin concepts and immersive experience using open-source tools and affordable hardware. Therefore, it can be easily used by Indonesian companies who plan to implement industry 4.0. This platform consists of three parts, 3D Object Management, VE Module, and VE Interface. The developed platform has successfully demonstrated how a physical device can be integrated with its virtual model using a Digital Twin. With this result, industry stakeholders can learn and try to develop digital twin platforms in their industry using this design.}, keywords = {Cyber-physical systems, digital twins, virtual engineering}, pubstate = {published}, tppubtype = {article} } Many industries in Indonesia face several challenges to adopting new technologies of industry 4.0, especially for Digital Twin (DT) and Virtual Engineering (VE), which are integrated with Cyber-Physical Systems (CPS). Lack of human resource and high costs are still significant challenges to developing DT and VE. This preliminary research aims to introduce the initial design of a platform to create Virtual Engineering applications that combine Digital Twin concepts and immersive experience using open-source tools and affordable hardware. Therefore, it can be easily used by Indonesian companies who plan to implement industry 4.0. This platform consists of three parts, 3D Object Management, VE Module, and VE Interface. The developed platform has successfully demonstrated how a physical device can be integrated with its virtual model using a Digital Twin. With this result, industry stakeholders can learn and try to develop digital twin platforms in their industry using this design. |
Bai Xuqi; Wicaksono, Hendro How Relevant Are Environmental Factors in The Ergonomic Performance Assessments? Journal Article Procedia Manufacturing, 52 , pp. 325-330, 2020. Abstract | Links | BibTeX | Tags: ergonomics, manufacturing, resource efficient manufacturing @article{Wicaksono2020c, title = {How Relevant Are Environmental Factors in The Ergonomic Performance Assessments?}, author = {Bai, Xuqi; Wicaksono, Hendro }, url = {https://doi.org/10.1016/j.promfg.2020.11.054}, doi = {10.1016/j.promfg.2020.11.054}, year = {2020}, date = {2020-12-31}, journal = {Procedia Manufacturing}, volume = {52}, pages = {325-330}, abstract = {A suitable working environment is crucial to ensure the worker’s safety and health, results in higher productivity in production systems. As one of the most important elements of production, the assembly processes require the most human involvement. However, researches on ergonomics in assembly systems focus merely on task-related physical factors such as action forces, posture, movement, task repetition, etc. This paper aims to investigate the relevance of environmental measures of temperature, humidity, ventilation, noise, lighting, and cleanness to the assembly workers, and the relative importance of environmental factors in comparison with the task-related physical factors. A survey was conducted among 20 assembly workers and engineers in the hope to realize the urgency to integrate environmental characteristics into routinely conducted ergonomic performance assessments.}, keywords = {ergonomics, manufacturing, resource efficient manufacturing}, pubstate = {published}, tppubtype = {article} } A suitable working environment is crucial to ensure the worker’s safety and health, results in higher productivity in production systems. As one of the most important elements of production, the assembly processes require the most human involvement. However, researches on ergonomics in assembly systems focus merely on task-related physical factors such as action forces, posture, movement, task repetition, etc. This paper aims to investigate the relevance of environmental measures of temperature, humidity, ventilation, noise, lighting, and cleanness to the assembly workers, and the relative importance of environmental factors in comparison with the task-related physical factors. A survey was conducted among 20 assembly workers and engineers in the hope to realize the urgency to integrate environmental characteristics into routinely conducted ergonomic performance assessments. |
Angreani Linda Salma; Vijaya, Annas; Wicaksono Hendro Systematic Literature Review of Industry 4.0 Maturity Model for Manufacturing and Logistics Sectors Journal Article Procedia Manufacturing, 52 , 2020. Abstract | Links | BibTeX | Tags: industry 4.0, industry 4.0 maturity assessment, manufacturing, systematic literature review @article{Angreani2020, title = {Systematic Literature Review of Industry 4.0 Maturity Model for Manufacturing and Logistics Sectors}, author = {Angreani, Linda Salma; Vijaya, Annas; Wicaksono, Hendro}, url = {https://doi.org/10.1016/j.promfg.2020.11.056}, doi = {doi.org/10.1016/j.promfg.2020.11.056}, year = {2020}, date = {2020-12-31}, journal = {Procedia Manufacturing}, volume = {52}, abstract = {A maturity model is a wide technique to measure several aspects and identify the current state of processes in an organization, which can be used as a starting point for business improvement. In the Industry 4.0 context, several terms are used to express the model, such as readiness assessment model, roadmap, framework, and maturity index. They have the same purpose of measuring how the current state of an organization unit is capable of adopting and implementing the concept of industry 4.0 in the future. Many researchers had proposed maturity models for assessing Industry 4.0 readiness and maturity since 2011 when Industry 4.0 was commenced. However, there has been no attempt to analyze empirical evidence systematically. This paper aims to analyze currently available maturity models related to Industry 4.0 and provide a synthesis on those maturity models. This paper describes a systematic literature review (SLR) of empirical studies implemented on the maturity model published in several reputable and relevant sources. It focuses on the manufacturing and logistics sectors since the processes in both sectors can be highly improved through the introduction of technologies such as cyber-physical systems, internet of things, and artificial intelligence. In general, the primary purpose of the review is to address the following questions: (1) Based on what dimensions do researchers develop Industry 4.0 maturity models, and what are the most used and influencing parameters in those dimensions? (2) How do those maturity models compare to each other in terms of dimension complexity, techniques, maturity leveling, and kind of application sectors of the model? In conclusion, the maturity model in the context of Industry 4.0 is promising to guide the adoption of industry 4.0 technologies at the organization level. However, just having a maturity model is not enough. More efforts are needed to facilitate the application of it.}, keywords = {industry 4.0, industry 4.0 maturity assessment, manufacturing, systematic literature review}, pubstate = {published}, tppubtype = {article} } A maturity model is a wide technique to measure several aspects and identify the current state of processes in an organization, which can be used as a starting point for business improvement. In the Industry 4.0 context, several terms are used to express the model, such as readiness assessment model, roadmap, framework, and maturity index. They have the same purpose of measuring how the current state of an organization unit is capable of adopting and implementing the concept of industry 4.0 in the future. Many researchers had proposed maturity models for assessing Industry 4.0 readiness and maturity since 2011 when Industry 4.0 was commenced. However, there has been no attempt to analyze empirical evidence systematically. This paper aims to analyze currently available maturity models related to Industry 4.0 and provide a synthesis on those maturity models. This paper describes a systematic literature review (SLR) of empirical studies implemented on the maturity model published in several reputable and relevant sources. It focuses on the manufacturing and logistics sectors since the processes in both sectors can be highly improved through the introduction of technologies such as cyber-physical systems, internet of things, and artificial intelligence. In general, the primary purpose of the review is to address the following questions: (1) Based on what dimensions do researchers develop Industry 4.0 maturity models, and what are the most used and influencing parameters in those dimensions? (2) How do those maturity models compare to each other in terms of dimension complexity, techniques, maturity leveling, and kind of application sectors of the model? In conclusion, the maturity model in the context of Industry 4.0 is promising to guide the adoption of industry 4.0 technologies at the organization level. However, just having a maturity model is not enough. More efforts are needed to facilitate the application of it. |
Wicaksono Hendro; Ni, Tianran An Automated Information System for Medium to Short-Term Manpower Capacity Planning in Make-To-Order Manufacturing Journal Article Procedia Manufacturing, 52 , pp. 319-324, 2020. Abstract | Links | BibTeX | Tags: information systems, Information visualisation, production planning and control, resource efficient manufacturing @article{Wicaksono2020d, title = {An Automated Information System for Medium to Short-Term Manpower Capacity Planning in Make-To-Order Manufacturing}, author = {Wicaksono, Hendro; Ni, Tianran}, url = {https://doi.org/10.1016/j.promfg.2020.11.053}, doi = {10.1016/j.promfg.2020.11.053}, year = {2020}, date = {2020-12-31}, journal = {Procedia Manufacturing}, volume = {52}, pages = {319-324}, abstract = {In today’s tough economy, it is important for (Make-To-Order) MTO companies to be responsive to customer demand and market fluctuations and to keep the costs as low as possible at the same time. Unlike in Make-To-Stock (MTS), MTO companies hold capacity in reserve. Thus, they are able to make efficient utilization of available capacity to satisfy customer needs. This then leads to a constant capacity planning problem. The companies are facing fluctuations between overload by lack of sufficient capacity, and idleness by excess of capacity comparing to the level of demand. Among all the planning resources, the available manpower is one of the most essential parts of the MTO operations. Therefore, the allocation and adjustment of manpower capacity that suits different planning horizons is a predominant measure to meet the changing capacity demands. Nonetheless, a signing each individual labor to various types of tasks and orders on a day to day basis continually for the planning horizon of several weeks or months is difficult. This paper presents an approach of automated manpower planning model which can be used by MTO operations to achieve a better transparency and synchronization of capacity load for short to medium planning horizons. The approach is implemented as a software tool to automate the data processing and analysis, which helps to dramatically reduce the corresponding data operation efforts and planning time. This paper also presents the validation of the approach and tool in a real production unit in a German small MTO manufacturing company.}, keywords = {information systems, Information visualisation, production planning and control, resource efficient manufacturing}, pubstate = {published}, tppubtype = {article} } In today’s tough economy, it is important for (Make-To-Order) MTO companies to be responsive to customer demand and market fluctuations and to keep the costs as low as possible at the same time. Unlike in Make-To-Stock (MTS), MTO companies hold capacity in reserve. Thus, they are able to make efficient utilization of available capacity to satisfy customer needs. This then leads to a constant capacity planning problem. The companies are facing fluctuations between overload by lack of sufficient capacity, and idleness by excess of capacity comparing to the level of demand. Among all the planning resources, the available manpower is one of the most essential parts of the MTO operations. Therefore, the allocation and adjustment of manpower capacity that suits different planning horizons is a predominant measure to meet the changing capacity demands. Nonetheless, a signing each individual labor to various types of tasks and orders on a day to day basis continually for the planning horizon of several weeks or months is difficult. This paper presents an approach of automated manpower planning model which can be used by MTO operations to achieve a better transparency and synchronization of capacity load for short to medium planning horizons. The approach is implemented as a software tool to automate the data processing and analysis, which helps to dramatically reduce the corresponding data operation efforts and planning time. This paper also presents the validation of the approach and tool in a real production unit in a German small MTO manufacturing company. |
Wicaksono, Simon Fritz; Matthias Jaenicke; Jivka Ovtcharova; Hendro Context-sensitive Assistance in Requirements-based Knowledge Management Conference NLPIR 2020: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, ACM, 2020. Abstract | Links | BibTeX | Tags: knowledge management, machine learning, requirement engineering @conference{Wicaksono2020, title = {Context-sensitive Assistance in Requirements-based Knowledge Management}, author = {Simon Fritz; Matthias Jaenicke; Jivka Ovtcharova; Hendro Wicaksono }, url = {https://doi.org/10.1145/3443279.3443306}, doi = {10.1145/3443279.3443306}, year = {2020}, date = {2020-12-17}, booktitle = {NLPIR 2020: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval}, pages = {47–54}, publisher = {ACM}, abstract = {In this paper, a concept of a digital assistance system is presented which, based on computer linguistic methods, supports the user in the tasks of requirement-based knowledge management. The concept is divided into six modules that offer context-sensitive support in the identification, documentation, linking, modification and reuse of requirements and the associated knowledge. Since this concept was developed as part of the BMBF-funded SME Innovative Project DAM4KMU, which is primarily aimed at German SMEs, the concept developed was specially designed for processing German-language texts. The digital assistance system pursues the goal, on the one hand, of increasing the quality of the documentation by supporting the user in the creation of complete formulations. On the other hand, with the help of the most modern language models, possible relationships between the information should be identified and linked to each other in a partially automated manner. In addition, the integration of web crawling technologies should make the knowledge available on the Internet available in a context-sensitive manner, in order to lift possible innovations on the one hand and not to forget possible non-considered boundary conditions on the other. The automatic linking of all information is intended to ensure a continuous exchange of knowledge, which should reduce misunderstandings and non-communicated changes to requirements or goals to a minimum. }, keywords = {knowledge management, machine learning, requirement engineering}, pubstate = {published}, tppubtype = {conference} } In this paper, a concept of a digital assistance system is presented which, based on computer linguistic methods, supports the user in the tasks of requirement-based knowledge management. The concept is divided into six modules that offer context-sensitive support in the identification, documentation, linking, modification and reuse of requirements and the associated knowledge. Since this concept was developed as part of the BMBF-funded SME Innovative Project DAM4KMU, which is primarily aimed at German SMEs, the concept developed was specially designed for processing German-language texts. The digital assistance system pursues the goal, on the one hand, of increasing the quality of the documentation by supporting the user in the creation of complete formulations. On the other hand, with the help of the most modern language models, possible relationships between the information should be identified and linked to each other in a partially automated manner. In addition, the integration of web crawling technologies should make the knowledge available on the Internet available in a context-sensitive manner, in order to lift possible innovations on the one hand and not to forget possible non-considered boundary conditions on the other. The automatic linking of all information is intended to ensure a continuous exchange of knowledge, which should reduce misunderstandings and non-communicated changes to requirements or goals to a minimum. |
Agustia Dian; Sriani, Dewi; Wicaksono Hendro; Gani Lindawati Integrated reporting quality assessment Journal Article Journal of Security and Sustainability Issues, 10 (1), pp. 47-59, 2020. BibTeX | Tags: integrated assessment @article{Agustia2020, title = {Integrated reporting quality assessment}, author = {Agustia, Dian; Sriani, Dewi; Wicaksono, Hendro; Gani, Lindawati}, year = {2020}, date = {2020-09-01}, journal = {Journal of Security and Sustainability Issues}, volume = {10}, number = {1}, pages = {47-59}, keywords = {integrated assessment}, pubstate = {published}, tppubtype = {article} } |
2019 |
Rasyid Alfandino; Ulil Albaab, Mochammad Rifki; Falah Muhammad Fajrul Panduman Yohanes Yohanie Fridelin; Yusuf Alviansyah Arman; Basuki Dwi Kurnia; Tjahjono Anang; Budiarti Rizqi Putri Nourma; Sukaridhoto Sritrusta; Yudianto Firman; Wicaksono Hendro ; Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform Inproceedings 2019 International Electronics Symposium (IES), pp. 672-675, IEEE, 2019. Links | BibTeX | Tags: industry 4.0, Internet of Things, machine learning @inproceedings{Rasyid2019, title = {Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform}, author = {Rasyid, Alfandino; Ulil Albaab, Mochammad Rifki; Falah, Muhammad Fajrul ; Panduman, Yohanes Yohanie Fridelin; Yusuf, Alviansyah Arman; Basuki, Dwi Kurnia; Tjahjono, Anang; Budiarti, Rizqi Putri Nourma; Sukaridhoto, Sritrusta; Yudianto, Firman; Wicaksono, Hendro}, url = {https://ieeexplore.ieee.org/document/8901626}, doi = {10.1109/ELECSYM.2019.8901626}, year = {2019}, date = {2019-09-28}, booktitle = { 2019 International Electronics Symposium (IES)}, pages = {672-675}, publisher = {IEEE}, keywords = {industry 4.0, Internet of Things, machine learning}, pubstate = {published}, tppubtype = {inproceedings} } |
Rasyid Alfandino; Ulil Albaab, Mochammad Rifki; Falah Muhammad Fajrul; Panduman Yohanes Yohanie Fridelin; Yusuf Alviansyah Arman; Basuki Dwi Kurnia; Tjahjono Anang; Budiarti Rizqi Putri Nourma; Sukaridhoto Sritrusta; Yudianto Firman; Wicaksono Hendro Pothole visual detection using machine learning method integrated with internet of thing video streaming platform Conference 2019 International Electronics Symposium (IES) , 2019. BibTeX | Tags: machine learning, virtual engineering @conference{Rasyid2019b, title = {Pothole visual detection using machine learning method integrated with internet of thing video streaming platform}, author = {Rasyid, Alfandino; Ulil Albaab, Mochammad Rifki; Falah, Muhammad Fajrul; Panduman, Yohanes Yohanie Fridelin; Yusuf, Alviansyah Arman; Basuki, Dwi Kurnia; Tjahjono, Anang; Budiarti, Rizqi Putri Nourma; Sukaridhoto, Sritrusta; Yudianto, Firman; Wicaksono, Hendro}, year = {2019}, date = {2019-09-28}, booktitle = {2019 International Electronics Symposium (IES) }, keywords = {machine learning, virtual engineering}, pubstate = {published}, tppubtype = {conference} } |
Kusumawardana Arya; Habibi, Muhammad Afnan; Wibawanto Slamet; Wicaksono Hendro; Prasetya Yoga; Nurrahman Rizqi Coordination Power Control Of DC Water Pump System using Dual-loop Control and Consensus Algorithm Inproceedings 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE), pp. 37-42, IEEE, 2019. Links | BibTeX | Tags: algorithm, artificial intelligence, energy efficiency, Energy efficient building @inproceedings{Kusumawardana2020, title = {Coordination Power Control Of DC Water Pump System using Dual-loop Control and Consensus Algorithm}, author = {Kusumawardana, Arya; Habibi, Muhammad Afnan; Wibawanto,Slamet; Wicaksono, Hendro; Prasetya, Yoga; Nurrahman, Rizqi }, url = {https://ieeexplore.ieee.org/document/8981473}, doi = {10.1109/ICEEIE47180.2019.8981473}, year = {2019}, date = {2019-02-01}, booktitle = {2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE)}, pages = {37-42}, publisher = {IEEE}, keywords = {algorithm, artificial intelligence, energy efficiency, Energy efficient building}, pubstate = {published}, tppubtype = {inproceedings} } |
Schneider Georg Ferdinand; Wicaksono, Hendro; Ovtcharova Jivka Virtual engineering of cyber-physical automation systems: The case of control logic Journal Article Advanced Engineering Informatics, 39 , pp. 127-143, 2019, ISBN: 1474-0346. Abstract | Links | BibTeX | Tags: Cyber-physical systems, industry 4.0, Ontology, virtual engineering @article{Schneider2019, title = {Virtual engineering of cyber-physical automation systems: The case of control logic}, author = {Schneider, Georg Ferdinand; Wicaksono, Hendro; Ovtcharova, Jivka }, url = {https://www.sciencedirect.com/science/article/pii/S1474034618300740}, doi = {https://doi.org/10.1016/j.aei.2018.11.009.}, isbn = {1474-0346}, year = {2019}, date = {2019-01-31}, journal = {Advanced Engineering Informatics}, volume = {39}, pages = {127-143}, abstract = {Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner.}, keywords = {Cyber-physical systems, industry 4.0, Ontology, virtual engineering}, pubstate = {published}, tppubtype = {article} } Mastering the fusion of information and communication technologies with physical systems to cyber-physical automation systems is of main concern to engineers in the industrial automation domain. The engineering of these systems is challenging as their distributed nature and the heterogeneity of stakeholders and tools involved in their engineering contradict the need for the simultaneous engineering of their cyber and physical parts over their life cycle. This paper presents a novel approach based on the virtual engineering method, which provides support for the simultaneous engineering of the cyber and physical parts of automation systems. The approach extends and integrates the life cycle centered view mandated by current conceptual architectures and the digital twin paradigm with an integrated, iterative engineering method. The benefits of the approach are highlighted in a case study related to the engineering of the control logic of a cyber physical automation system originating from the process engineering domain. We describe for the first time a modular domain ontology, which formally describes the cyber and physical part of the system. We present cyber services built on top of the ontology layer, which allow to automatically verify different control logic types and simultaneously verify cyber and physical parts of the system in an incremental manner. |
2018 |
Wicaksono, Hendro Challenges and Opportunities of Asian Logistics and Logistics 4.0 Workshop Intercoop Basel, 2018. BibTeX | Tags: industry 4.0, Internet of Things, logistics, logistics 4.0 @workshop{Wicaksono2018f, title = {Challenges and Opportunities of Asian Logistics and Logistics 4.0}, author = {Hendro Wicaksono}, year = {2018}, date = {2018-09-04}, address = {Basel}, organization = {Intercoop}, keywords = {industry 4.0, Internet of Things, logistics, logistics 4.0}, pubstate = {published}, tppubtype = {workshop} } |
Wicaksono, Hendro Preparing Indonesia and Germany for Industry 4.0: A Reflection and Inspirations from both countries Presentation 22.08.2018. BibTeX | Tags: health 4.0, industry 4.0, industry 4.0 maturity assessment @misc{Wicaksono2018c, title = {Preparing Indonesia and Germany for Industry 4.0: A Reflection and Inspirations from both countries }, author = {Wicaksono, Hendro}, year = {2018}, date = {2018-08-22}, keywords = {health 4.0, industry 4.0, industry 4.0 maturity assessment}, pubstate = {published}, tppubtype = {presentation} } |
Wicaksono, Hendro Research and innovation on Industry 4.0 Technologies Workshop PENS Surabaya, Surabaya, Indonesia, 2018. BibTeX | Tags: Agriculture 4.0, industry 4.0, innovation management, Internet of Things, Media 4.0, smart cities, Supply Chain 4.0 @workshop{Wicaksono2018e, title = {Research and innovation on Industry 4.0 Technologies}, author = {Wicaksono, Hendro}, year = {2018}, date = {2018-08-16}, publisher = {PENS Surabaya}, address = {Surabaya, Indonesia}, keywords = {Agriculture 4.0, industry 4.0, innovation management, Internet of Things, Media 4.0, smart cities, Supply Chain 4.0}, pubstate = {published}, tppubtype = {workshop} } |
Wicaksono, Hendro Preparing IT Industry and Organizations Towards industry 4.0 Workshop The Government of East Java Province, Indonesia, Surabaya, Indonesia, 2018. BibTeX | Tags: education 4.0, industry 4.0, industry 4.0 maturity assessment, innovation management, innovation network @workshop{Wicaksono2018d, title = {Preparing IT Industry and Organizations Towards industry 4.0}, author = {Wicaksono, Hendro }, editor = {The Government of East Java Province, Indonesia}, year = {2018}, date = {2018-08-14}, publisher = {The Government of East Java Province, Indonesia}, address = {Surabaya, Indonesia}, keywords = {education 4.0, industry 4.0, industry 4.0 maturity assessment, innovation management, innovation network}, pubstate = {published}, tppubtype = {workshop} } |
Howell Shaun; Wicaksono, Hendro; Yuce Baris; McGlinn Kris; Rezgui Yacine User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization Journal Article IEEE Transactions on Cybernetics, pp. 1-15, 2018, ISSN: 2168-2267. Abstract | Links | BibTeX | Tags: Artificial neural network, building energy management, data mining, Fuzzy logic, Genetic algorithm, middleware, Ontology, optimization, semantic web, WebGL @article{Howell2018, title = {User Centered Neuro-Fuzzy Energy Management Through Semantic-Based Optimization}, author = {Howell, Shaun; Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Rezgui, Yacine}, url = {https://ieeexplore.ieee.org/document/8412214/}, doi = {10.1109/TCYB.2018.2839700}, issn = {2168-2267}, year = {2018}, date = {2018-07-19}, journal = {IEEE Transactions on Cybernetics}, pages = {1-15}, abstract = {This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings.}, keywords = {Artificial neural network, building energy management, data mining, Fuzzy logic, Genetic algorithm, middleware, Ontology, optimization, semantic web, WebGL}, pubstate = {published}, tppubtype = {article} } This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system's intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings. |
Wicaksono, Hendro Material Ontology: A use case in energy management Workshop Materials Ontology Workshop, European Comission, European Comission - Directorate General for Research & Innovation, Directorate D - Industrial Technologies 2018. BibTeX | Tags: linked data, Ontology, vocabulary @workshop{Wicaksono2018b, title = {Material Ontology: A use case in energy management}, author = {Wicaksono, Hendro}, year = {2018}, date = {2018-06-29}, booktitle = {Materials Ontology Workshop, European Comission}, organization = {European Comission - Directorate General for Research & Innovation, Directorate D - Industrial Technologies}, keywords = {linked data, Ontology, vocabulary}, pubstate = {published}, tppubtype = {workshop} } |
Wicaksono, Hendro Eine Plattform für die ganzheitliche Smart-Energie-Lösung in Smart-City Presentation 09.04.2018. Abstract | Links | BibTeX | Tags: Energy efficient building, energy management, linked data, Ontology, semantic data integration, smart cities @misc{Wicaksono2018, title = {Eine Plattform für die ganzheitliche Smart-Energie-Lösung in Smart-City}, author = {Hendro Wicaksono }, editor = {RENEXPO Forum, Augsburg, 2018}, url = {http://www.renexpo.de/fuer-besucher/forum.html}, year = {2018}, date = {2018-04-09}, abstract = {Vortragsinhalte: - Was ist die eine ganzheitliche Energiemanagement-Plattform? - Was kann man mit der Plattform machen? - Die Anwendungen auf der Plattform - Die Lösungsansatz und Erweiterbarkeit - Anwendungserfahrungen in Städten und Kommunen }, keywords = {Energy efficient building, energy management, linked data, Ontology, semantic data integration, smart cities}, pubstate = {published}, tppubtype = {presentation} } Vortragsinhalte: - Was ist die eine ganzheitliche Energiemanagement-Plattform? - Was kann man mit der Plattform machen? - Die Anwendungen auf der Plattform - Die Lösungsansatz und Erweiterbarkeit - Anwendungserfahrungen in Städten und Kommunen |
Tonev, Kiril; Kappe, Simon; Krahtova, Preslava; Wicaksono, Hendro; Ovtcharova, Jivka District-Scale Data Integration by Leveraging Semantic Web Technologies: a Case in Smart Cities Book Chapter Christophe Debruyne Hervé Panetto, Georg Weichhart Peter Bollen Ioana Ciuciu Maria-Esther Vidal Robert Meersman (Ed.): pp. 289-292, Springer, 2018, ISBN: 9783319738055. Abstract | Links | BibTeX | Tags: building energy simulation, ontology alignment, semantic data integration, smart cities @inbook{Tonev2018, title = {District-Scale Data Integration by Leveraging Semantic Web Technologies: a Case in Smart Cities}, author = {Kiril Tonev and Simon Kappe and Preslava Krahtova and Hendro Wicaksono and Jivka Ovtcharova}, editor = {Christophe Debruyne, Hervé Panetto, Georg Weichhart, Peter Bollen, Ioana Ciuciu, Maria-Esther Vidal, Robert Meersman}, url = {https://www.springer.com/us/book/9783319738048}, isbn = {9783319738055}, year = {2018}, date = {2018-03-04}, pages = {289-292}, publisher = {Springer}, abstract = {Technologies of the Semantic Web stack promise to alleviate some of the challenges related to data integration on a massive scale and high level heterogenity. Thus paper explores their application in the smart cities domain with a focus on energy efficient districts. We develop an ontology grounded in several well-established vocabularies to leverage their shared semantics and facilitate data interoperability and we apply the developed ontology to integrate state-of-the-art energy simulation facilities into a general district-level monitoring framework.}, keywords = {building energy simulation, ontology alignment, semantic data integration, smart cities}, pubstate = {published}, tppubtype = {inbook} } Technologies of the Semantic Web stack promise to alleviate some of the challenges related to data integration on a massive scale and high level heterogenity. Thus paper explores their application in the smart cities domain with a focus on energy efficient districts. We develop an ontology grounded in several well-established vocabularies to leverage their shared semantics and facilitate data interoperability and we apply the developed ontology to integrate state-of-the-art energy simulation facilities into a general district-level monitoring framework. |
Haas, Klemens; Kappe, Simon; Siebert, Martin; Wicaksono, Hendro; Ovtcharova, Jivka Digital Assistance Based on an Ontology Driven Model of the IT-Systems Along the Product Lifecycle Book Chapter Debruyne, Christophe; Panetto, Hervé; Weichhart, Georg; Bollen, Peter; Ciuciu, Ioana; Vidal, Maria-Esther; Meersman, Robert (Ed.): Springer, 2018, ISBN: 978-3-319-73805-5. Abstract | Links | BibTeX | Tags: digitization, Ontology, product lifecycle management @inbook{Haas2018, title = {Digital Assistance Based on an Ontology Driven Model of the IT-Systems Along the Product Lifecycle}, author = {Klemens Haas and Simon Kappe and Martin Siebert and Hendro Wicaksono and Jivka Ovtcharova}, editor = {Christophe Debruyne and Hervé Panetto and Georg Weichhart and Peter Bollen and Ioana Ciuciu and Maria-Esther Vidal and Robert Meersman}, url = {https://www.springer.com/us/book/9783319738048}, doi = {10.1007/978-3-319-73805-5}, isbn = {978-3-319-73805-5}, year = {2018}, date = {2018-03-04}, publisher = {Springer}, abstract = {The market of Product Lifecycle Management (PLM) applications has changed into a complex landscape of heterogeneous systems in recent years. Consequently, it has become increasingly challenging for enterprises to identify a PLM application that meets their requirements and that can be successfully integrated into their existing IT systems. The approach presented in this paper aims at developing a decision supporting model of the IT system landscape that provides different analysis tools based on existing IT systems. The model which is expressed by an ontology is intended to represent data flows between the different IT applications in order to provide relevant information through requests and rules in further proceedings.}, keywords = {digitization, Ontology, product lifecycle management}, pubstate = {published}, tppubtype = {inbook} } The market of Product Lifecycle Management (PLM) applications has changed into a complex landscape of heterogeneous systems in recent years. Consequently, it has become increasingly challenging for enterprises to identify a PLM application that meets their requirements and that can be successfully integrated into their existing IT systems. The approach presented in this paper aims at developing a decision supporting model of the IT system landscape that provides different analysis tools based on existing IT systems. The model which is expressed by an ontology is intended to represent data flows between the different IT applications in order to provide relevant information through requests and rules in further proceedings. |
2017 |
Wicaksono, Hendro Best practices of university IT policies and governance Workshop UIN Surabaya Surabaya,Indonesia, 2017. BibTeX | Tags: IT govenrnance, IT management, software engineering @workshop{Wicaksono2017b, title = {Best practices of university IT policies and governance}, author = {Hendro Wicaksono }, year = {2017}, date = {2017-12-28}, address = {Surabaya,Indonesia}, organization = {UIN Surabaya}, keywords = {IT govenrnance, IT management, software engineering}, pubstate = {published}, tppubtype = {workshop} } |
Wicaksono, Hendro Welcoming industry 4.0 through synergy between higher education, business, and government: a perspective from Germany Presentation 19.12.2017. Links | BibTeX | Tags: industry 4.0, IT education, social education @misc{Wicaksono2017d, title = {Welcoming industry 4.0 through synergy between higher education, business, and government: a perspective from Germany}, author = {Hendro Wicaksono}, url = {http://uin-suka.ac.id/page/berita/detail/1551}, year = {2017}, date = {2017-12-19}, keywords = {industry 4.0, IT education, social education}, pubstate = {published}, tppubtype = {presentation} } |
Wicaksono, Hendro Towards high quality and social-aware nation of Indonesia 2030 - An Inspiration from Germany Presentation 18.12.2017. Links | BibTeX | Tags: industry 4.0, IT education, social education @misc{Wicaksono2017c, title = {Towards high quality and social-aware nation of Indonesia 2030 - An Inspiration from Germany}, author = {Hendro Wicaksono}, url = {http://brianrakhmataji.me/blog/mengenal-industry-4-dot-0-bersama-dr-ing-hendro-wicaksono-dalam-seminar-informatika-2017/}, year = {2017}, date = {2017-12-18}, keywords = {industry 4.0, IT education, social education}, pubstate = {published}, tppubtype = {presentation} } |
Wicaksono, Hendro DAREED: IT-Plattform für Energieeffizienz in Smart-City Workshop CEB Karlsruhe, 2017. Links | BibTeX | Tags: energy efficiency, IT integration, IT platform, semantic data integration, smart cities, smart energy @workshop{Wicaksono2017e, title = {DAREED: IT-Plattform für Energieeffizienz in Smart-City}, author = {Hendro Wicaksono}, url = {https://www.buildingsmart.de/kos/WNetz?art=File.download&id=6388&name=CEB17-Kongressprogramm.pdf http://presse.karlsruhe.de/db/meldungen/wirtschaft/best_practice_beispiele_fur_unternehmen.html}, year = {2017}, date = {2017-06-29}, address = {Karlsruhe}, organization = {CEB}, keywords = {energy efficiency, IT integration, IT platform, semantic data integration, smart cities, smart energy}, pubstate = {published}, tppubtype = {workshop} } |
McGlinn, Kris; Yuce, Baris; Wicaksono, Hendro; Howell, Shaun; Rezgui, Yacine Usability evaluation of a web-based tool for supporting holistic building energy management Journal Article Automation in Construction, 84 , pp. 154 - 165, 2017. Abstract | Links | BibTeX | Tags: Artificial neural network, BEMS, Fuzzy logic, Genetic algorithm, IFC, Information visualisation, Ontology @article{MCGLINN2017154, title = {Usability evaluation of a web-based tool for supporting holistic building energy management}, author = {Kris McGlinn and Baris Yuce and Hendro Wicaksono and Shaun Howell and Yacine Rezgui}, url = {https://www.sciencedirect.com/science/article/pii/S0926580516303545}, doi = {https://doi.org/10.1016/j.autcon.2017.08.033}, year = {2017}, date = {2017-03-31}, journal = {Automation in Construction}, volume = {84}, pages = {154 - 165}, abstract = {This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken.}, keywords = {Artificial neural network, BEMS, Fuzzy logic, Genetic algorithm, IFC, Information visualisation, Ontology}, pubstate = {published}, tppubtype = {article} } This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken. |
Wicaksono, Hendro Einsatz von (Open)-Linked-Data für Gebäudeinformationsmodellierung Inproceedings GmbH, Open Experience (Ed.): Forum Digitale Transformation des Baubetriebs in der Praxis, openexperience.de, 2017. Links | BibTeX | Tags: building energy management, linked data @inproceedings{Wicaksono2017, title = {Einsatz von (Open)-Linked-Data für Gebäudeinformationsmodellierung}, author = {Hendro Wicaksono }, editor = {Open Experience GmbH}, url = {https://openexperience.de/Forum_Digitale_Transformation_des_Baubetriebs_in_der_Praxis.html#Tagungsband}, year = {2017}, date = {2017-01-19}, booktitle = {Forum Digitale Transformation des Baubetriebs in der Praxis}, volume = {1}, publisher = {openexperience.de}, keywords = {building energy management, linked data}, pubstate = {published}, tppubtype = {inproceedings} } |
2016 |
McGlinn, Kris; Wiese, Matthias; Wicaksono, Hendro Towards a shared use case repository – the SWIMing initiative started in the framework of the EU H2020 R&DI programme Inproceedings Proceedings of the 33rd International Conference of CIB W78, Digital library of construction informatics and information technology in civil engineering and construction, 2016. Abstract | Links | BibTeX | Tags: building information modelling, Energy efficient building, linked data, Ontology @inproceedings{McGlinn2016, title = {Towards a shared use case repository – the SWIMing initiative started in the framework of the EU H2020 R&DI programme}, author = {Kris McGlinn and Matthias Wiese and Hendro Wicaksono}, url = {http://itc.scix.net/data/works/att/w78-2016-paper-010.pdf}, year = {2016}, date = {2016-11-02}, booktitle = {Proceedings of the 33rd International Conference of CIB W78}, publisher = {Digital library of construction informatics and information technology in civil engineering and construction}, abstract = {Data exchange and data sharing are one of the big challenges in the Architecture, Engineering, and Construction (AEC) industry and energy efficient building (EeB) domain. BIM open standards and lately the use of Semantic Web technologies provide a sound basis to implement exchange requirements derived from typical EeB use cases. However, the challenge remains to identify what models are available and how to align these with a particular use cases data requirements. This paper focuses on the application of an established methodology (Information Delivery Manual) adapted for the EeB domain and the application of the BIM*Q tool, which applies this methodology. The paper proposes to build-up a shared use case repository that collects detailed data Exchange Requirements as well as alignments to existing models to support projects when developing new use cases in the difficult task of aligning data requirements with models and standards. }, keywords = {building information modelling, Energy efficient building, linked data, Ontology}, pubstate = {published}, tppubtype = {inproceedings} } Data exchange and data sharing are one of the big challenges in the Architecture, Engineering, and Construction (AEC) industry and energy efficient building (EeB) domain. BIM open standards and lately the use of Semantic Web technologies provide a sound basis to implement exchange requirements derived from typical EeB use cases. However, the challenge remains to identify what models are available and how to align these with a particular use cases data requirements. This paper focuses on the application of an established methodology (Information Delivery Manual) adapted for the EeB domain and the application of the BIM*Q tool, which applies this methodology. The paper proposes to build-up a shared use case repository that collects detailed data Exchange Requirements as well as alignments to existing models to support projects when developing new use cases in the difficult task of aligning data requirements with models and standards. |