2025
Fekete, Tamas; Wicaksono, Hendro
Ontology-guided causal discovery and inference for reducing CO2 emissions in transportation Journal Article
In: International Journal of Sustainable Transportation, pp. 1–21, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, causal AI, causal inference, machine learning, ontologies, semantic web, sustainability, transportation
@article{fekete2025ontology,
title = {Ontology-guided causal discovery and inference for reducing CO2 emissions in transportation},
author = {Tamas Fekete and Hendro Wicaksono},
url = {https://www.tandfonline.com/eprint/4CUZX6ZZ5J8R8UCGIUEH/full?target=10.1080/15568318.2025.2588608},
doi = {https://doi.org/10.1080/15568318.2025.2588608},
year = {2025},
date = {2025-12-07},
urldate = {2025-12-07},
journal = {International Journal of Sustainable Transportation},
pages = {1–21},
publisher = {Taylor & Francis},
abstract = {This study investigates how ontology-guided causal discovery can be applied to reduce CO2 emissions in transportation. The analysis uses a cross-sectional dataset of 463,568 passenger vehicles inspected in Hungary between January and March 2023, which includes key technical attributes such as engine performance, cylinder capacity, and drive-by noise levels. Using causal discovery algorithms (PC, FCI, and GES) with and without ontology-based constraints, directed acyclic graphs are constructed to identify structural relationships among these variables and CO2 emissions. For causal inference, effect sizes of engine performance and other technical characteristics on emissions are estimated while considering potential confounding factors. The findings show that ontology-informed models improve both the plausibility and interpretability of the discovered causal structures, though limitations remain regarding unobserved variables and nonlinear relationships; accordingly, this validation-focused case study provides a foundation for extensions to behavior-driven contexts (e.g. usage patterns, compliance, market responses) where causal structure and effect magnitudes are more uncertain in advance. The results indicate that cylinder capacity and specific power (engine performance at fixed displacement) are among the strongest contributors to CO2 emissions, with ontology constraints reducing spurious links and increasing robustness across algorithms. Policy implications include the need for regulatory measures that integrate domain knowledge into emissions assessments, as well as the importance of updating technical standards and testing frameworks to reflect causal interactions rather than simple correlations. These insights can support more reliable interventions to lower vehicle-related emissions and contribute to sustainable transportation strategies.},
keywords = {artificial intelligence, causal AI, causal inference, machine learning, ontologies, semantic web, sustainability, transportation},
pubstate = {published},
tppubtype = {article}
}
This study investigates how ontology-guided causal discovery can be applied to reduce CO2 emissions in transportation. The analysis uses a cross-sectional dataset of 463,568 passenger vehicles inspected in Hungary between January and March 2023, which includes key technical attributes such as engine performance, cylinder capacity, and drive-by noise levels. Using causal discovery algorithms (PC, FCI, and GES) with and without ontology-based constraints, directed acyclic graphs are constructed to identify structural relationships among these variables and CO2 emissions. For causal inference, effect sizes of engine performance and other technical characteristics on emissions are estimated while considering potential confounding factors. The findings show that ontology-informed models improve both the plausibility and interpretability of the discovered causal structures, though limitations remain regarding unobserved variables and nonlinear relationships; accordingly, this validation-focused case study provides a foundation for extensions to behavior-driven contexts (e.g. usage patterns, compliance, market responses) where causal structure and effect magnitudes are more uncertain in advance. The results indicate that cylinder capacity and specific power (engine performance at fixed displacement) are among the strongest contributors to CO2 emissions, with ontology constraints reducing spurious links and increasing robustness across algorithms. Policy implications include the need for regulatory measures that integrate domain knowledge into emissions assessments, as well as the importance of updating technical standards and testing frameworks to reflect causal interactions rather than simple correlations. These insights can support more reliable interventions to lower vehicle-related emissions and contribute to sustainable transportation strategies.
Boroukhian, Tina; Supyen, Kritkorn; Mclaughlan, Christopher William; Bashyal, Atit; Pham, Tuan; Wicaksono, Hendro
Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing Journal Article
In: Computers in Industry, vol. 172, pp. 104354, 2025.
Abstract | Links | BibTeX | Tags: data management, demand response system, energy management, green energy, industry 4.0, industry 5.0, knowledge management, manufacturing, ontologies, semantic web, sustainability
@article{boroukhian2025semantic,
title = {Semantic middleware for demand response systems: Enhancing data interoperability in green electricity management for manufacturing},
author = {Tina Boroukhian and Kritkorn Supyen and Christopher William Mclaughlan and Atit Bashyal and Tuan Pham and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.compind.2025.104354},
year = {2025},
date = {2025-11-01},
urldate = {2025-01-01},
journal = {Computers in Industry},
volume = {172},
pages = {104354},
publisher = {Elsevier},
abstract = {Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.},
keywords = {data management, demand response system, energy management, green energy, industry 4.0, industry 5.0, knowledge management, manufacturing, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Optimizing the consumption of green electricity across sectors, including manufacturing, is a critical strategy for achieving net-zero emissions and advancing clean production in Europe by 2050. Demand Response (DR) represents a promising approach to engaging power consumers from all sectors in the transition toward increased utilization of renewable energy sources. A functional DR system for manufacturing power consumers requires seamless data integration and communication between information systems across multiple domains, including both power consumption and generation. This paper introduces a semantic middleware specifically designed for DR systems in the manufacturing sector, using an ontology as the central information model. To develop this ontology, we adopted a strategy that reuses and unifies existing ontologies from multiple domains, ensuring comprehensive coverage of the data requirements for DR applications in manufacturing. To operationalize this strategy, we designed novel methods for effective ontology unification and implemented them within a dedicated unification tool. This process was followed by data-to-ontology mapping to construct a knowledge graph, and was further extended through the development of a querying system equipped with a natural language interface. Additionally, this paper offers insights into the deployment environment of the semantic middleware, encompassing multiple data sources and the applications that utilize this data. The proposed approach is implemented in multiple German manufacturing small and medium-sized enterprises connected to a utility company, demonstrating consistent data interpretation and seamless information integration. Consequently, the method offers practical potential for optimizing green electricity usage in the manufacturing sector and supporting the transition toward a more sustainable and cleaner future.
Boroukhian, Tina; Supyen, Kritkorn; Samson, Jhealyn Bautista; Bashyal, Atit; Wicaksono, Hendro
Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 140, no. 9, pp. 4679–4711, 2025.
Abstract | Links | BibTeX | Tags: data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web
@article{boroukhian2025integrating,
title = {Integrating 3D object detection with ontologies for accurate digital twin creation in manufacturing systems},
author = {Tina Boroukhian and Kritkorn Supyen and Jhealyn Bautista Samson and Atit Bashyal and Hendro Wicaksono},
doi = {https://doi.org/10.1007/s00170-025-16548-x},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {140},
number = {9},
pages = {4679–4711},
publisher = {Springer London},
abstract = {The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.},
keywords = {data science, deep learning, digital twins, image processing, industry 4.0, machine learning, manufacturing, object detection, ontologies, semantic web},
pubstate = {published},
tppubtype = {article}
}
The digitization of manufacturing resources through digital twins (DTs) enhances operational efficiency and resource management. Ontologies play a key role in maintaining semantic consistency within DT systems. However, existing ontology-based approaches face challenges, including limited adaptability, integration of heterogeneous data—such as 3D images—and high manual effort in ontology development. These limitations hinder the scalability of DT implementations. Traditional 2D imaging often lacks spatial accuracy in complex manufacturing environments, causing inefficiencies and higher costs. Integrating richer data with intelligent frameworks is crucial for improving production and adaptability. The proposed study addresses these challenges by introducing a methodology that integrates existing ontologies with advanced 3D object detection models. The proposed approach employs two fully automated pipelines: one for detecting manufacturing resources from 3D images and another for mapping them into ontologies, ensuring seamless integration into DT frameworks. By leveraging established ontologies, the methodology enhances interoperability, reduces implementation complexity, and facilitates scalable deployment of DT systems across various industrial applications. Additionally, a comparative analysis of multiple advanced 3D detection models provides valuable insights to guide the selection of optimal solutions for diverse industrial settings. Experimental results show that YOLOv8 achieved the highest performance, with 91% classification accuracy, 86% precision, 81% recall, and the fastest inference time of 0.66 s. For ontology population, four machine labels—Robot, MillingMachine, BandSaw, and Lathe—were successfully integrated using a semantic similarity-based mapping strategy, enabling automated class creation and merging. This innovative framework sets a new benchmark for DT implementations, offering enhanced accuracy, efficiency, and semantic coherence in modern manufacturing.
Vijaya, Annas; Qadri, Faris Dzaudan; Angreani, Linda Salma; Wicaksono, Hendro
In: Resources, Environment and Sustainability, vol. 22, pp. 100262, 2025.
Abstract | Links | BibTeX | Tags: data management, data science, ESG, interoperability, ontologies, semantic web, sustainability
@article{vijaya2025esgont,
title = {ESGOnt: An ontology-based framework for Enhancing Environmental, Social, and Governance (ESG) assessments and aligning with Sustainable Development Goals (SDG)},
author = {Annas Vijaya and Faris Dzaudan Qadri and Linda Salma Angreani and Hendro Wicaksono},
doi = {https://doi.org/10.1016/j.resenv.2025.100262},
year = {2025},
date = {2025-08-25},
urldate = {2025-08-25},
journal = {Resources, Environment and Sustainability},
volume = {22},
pages = {100262},
publisher = {Elsevier},
abstract = {This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.},
keywords = {data management, data science, ESG, interoperability, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
This study proposes ESGOnt, an ontology-based framework that aligns Environmental, Social, and Governance (ESG) management with Sustainable Development Goals (SDGs). ESGOnt addresses key challenges in sustainable resource governance systems and cross-sector interoperability by providing a unified structure for ESG and SDG integration. The framework was developed through a systematic methodology that combines a literature review, standardization of ESG and SDG relationships, development of an adaptable maturity model, and ontology implementation using established methods such as Methontology and NeOn. ESGOnt enables the integration of diverse ESG taxonomies and ESG reporting standards, including GRI and ESRS, and assists companies in their ESG performance evaluation. Empirical validation through real-world use cases demonstrates its capability to (1) direct assessment of ESG assessments with specific SDG targets, such as SDG13 (Climate Action) and SDG 12 (Responsible Consumption and Production), (2) assess organizational ESG progress through different metrics, (3) facilitation of standardized and interoperable reporting for small and large enterprises, and (4) automatically validate organization compliance with EU Non-Financial Reporting Directive regulations. The findings show that ESGOnt resolves data inconsistency and transparency issues by enabling integrated and auditable sustainability reporting. The ontology-driven approach of the framework enables scalable and policy-relevant tools for tracking environmental and social impacts, while its maturity model focuses on strategic improvements in resource efficiency. Future studies will analyze and extend ESGOnt’s functionality for sector-specific capabilities, such as bioeconomy control systems, and explore advanced AI-driven inspection methods for real-time ESG-SDG assessment.
Wicaksono, Hendro; Trat, Martin; Bashyal, Atit; Boroukhian, Tina; Felder, Mine; Ahrens, Mischa; Bender, Janek; Groß, Sebastian; Steiner, Daniel; July, Christoph; others,
Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes Journal Article
In: The International Journal of Advanced Manufacturing Technology, vol. 138, pp. 247–271, 2025.
Abstract | Links | BibTeX | Tags: artificial intelligence, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability
@article{wicaksono2024artificial,
title = {Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes},
author = {Hendro Wicaksono and Martin Trat and Atit Bashyal and Tina Boroukhian and Mine Felder and Mischa Ahrens and Janek Bender and Sebastian Groß and Daniel Steiner and Christoph July and others},
url = {https://link.springer.com/article/10.1007/s00170-024-13372-7},
doi = {https://doi.org/10.1007/s00170-024-13372-7},
year = {2025},
date = {2025-05-01},
urldate = {2025-05-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {138},
pages = { 247–271},
publisher = {Springer London},
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, data management, data science, demand response system, energy management, green energy, industry 4.0, interoperability, machine learning, manufacturing, ontologies, reinforcement learning, semantic web, sustainability},
pubstate = {published},
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.
Angreani, Linda S; Vijaya, Annas; Wicaksono, Hendro
OntoMat 4.0: An Ontology Framework for Enhanced Industry 4.0 Maturity Assessment Journal Article
In: IEEE Access, vol. 13, pp. 68801-68819, 2025.
Abstract | Links | BibTeX | Tags: digital transformation, industry 4.0, interoperability, manufacturing, ontologies, semantic web, supply chain management
@article{angreani2025ontomat,
title = {OntoMat 4.0: An Ontology Framework for Enhanced Industry 4.0 Maturity Assessment},
author = {Linda S Angreani and Annas Vijaya and Hendro Wicaksono},
doi = {https://doi.org/10.1109/ACCESS.2025.3561229},
year = {2025},
date = {2025-04-15},
urldate = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {68801-68819},
publisher = {IEEE},
abstract = {Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.},
keywords = {digital transformation, industry 4.0, interoperability, manufacturing, ontologies, semantic web, supply chain management},
pubstate = {published},
tppubtype = {article}
}
Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.
2024
Prayitno, Kutut Aji; Wicaksono, Hendro
Investigating the Potential of Causal Reinforcement Learning in Collaborative Urban Logistics: A Systematic Literature Review Journal Article
In: Procedia CIRP, vol. 130, pp. 1070-1076, 2024.
Abstract | Links | BibTeX | Tags: causal AI, interoperability, logistics, ontologies, reinforcement learning, semantic web, sustainability
@article{nokey,
title = {Investigating the Potential of Causal Reinforcement Learning in Collaborative Urban Logistics: A Systematic Literature Review},
author = {Kutut Aji Prayitno and Hendro Wicaksono},
url = {https://www.sciencedirect.com/science/article/pii/S2212827124013659},
doi = {https://doi.org/10.1016/j.procir.2024.10.208},
year = {2024},
date = {2024-11-27},
urldate = {2024-11-27},
journal = {Procedia CIRP},
volume = {130},
pages = {1070-1076},
abstract = {Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.
},
keywords = {causal AI, interoperability, logistics, ontologies, reinforcement learning, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.
Supyen, Kritkorn; Mathur, Abhishek; Boroukhian, Tina; Wicaksono, Hendro
Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques Proceedings Article
In: International Conference on Dynamics in Logistics, pp. 419–430, Springer Nature Switzerland Cham 2024.
Abstract | Links | BibTeX | Tags: computer vision, digital twins, machine learning, ontologies, semantic web
@inproceedings{supyen2024streamlining,
title = {Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques},
author = {Kritkorn Supyen and Abhishek Mathur and Tina Boroukhian and Hendro Wicaksono},
url = {https://link.springer.com/chapter/10.1007/978-3-031-56826-8_32},
doi = {https://doi.org/10.1007/978-3-031-56826-8_32},
year = {2024},
date = {2024-04-03},
urldate = {2024-04-03},
booktitle = {International Conference on Dynamics in Logistics},
pages = {419–430},
organization = {Springer Nature Switzerland Cham},
abstract = {Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.
},
keywords = {computer vision, digital twins, machine learning, ontologies, semantic web},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.
2023
Wicaksono, Hendro; Nisa, Mehr Un; Vijaya, Annas
In: 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 0528–0532, IEEE 2023.
Abstract | Links | BibTeX | Tags: digital twins, explainable AI, interoperability, ontologies, semantic web, transportation
@inproceedings{wicaksono2023towards,
title = {Towards Intelligent and Trustable Digital Twin Asset Management Platform for Transportation Infrastructure Management Using Knowledge Graph and Explainable Artificial Intelligence (XAI)},
author = {Hendro Wicaksono and Mehr Un Nisa and Annas Vijaya},
doi = {https://doi.org/10.1109/IEEM58616.2023.10406401},
year = {2023},
date = {2023-12-18},
urldate = {2023-01-01},
booktitle = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
pages = {0528–0532},
organization = {IEEE},
abstract = {In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.},
keywords = {digital twins, explainable AI, interoperability, ontologies, semantic web, transportation},
pubstate = {published},
tppubtype = {inproceedings}
}
In the transportation sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as a lack of shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. This research develops an asset management platform suitable for Small and Medium Enterprises (SMEs) for the cross-company, secure, and intuitive collaborative management of digital twin assets. It can be achieved by developing an ontology-based semantic model of the assets, explainable machine learning (XAI), and a scenario-based intelligent search and discovery mechanism.
Pidikiti, Vamsi Sai; Vijaya, Annas; Valilai, Omid Fatahi; Wicaksono, Hendro
An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry Journal Article
In: Procedia CIRP, vol. 120, pp. 1351–1356, 2023.
BibTeX | Tags: circular economy, ontologies, semantic web, sustainability
@article{pidikiti2023ontology,
title = {An Ontology Model to Facilitate the Semantic Interoperability in Assessing the Circular Economy Performance of the Automotive Industry},
author = {Vamsi Sai Pidikiti and Annas Vijaya and Omid Fatahi Valilai and Hendro Wicaksono},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Procedia CIRP},
volume = {120},
pages = {1351–1356},
publisher = {Elsevier},
keywords = {circular economy, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
2022
Wicaksono, Hendro; Yuce, Baris; McGlinn, Kris; Calli, Ozum
Smart cities and buildings Book Section
In: Buildings and Semantics, pp. 25, CRC Press, 2022.
BibTeX | Tags: machine learning, ontologies, semantic web, smart cities, sustainability
@incollection{wicaksono2022smart,
title = {Smart cities and buildings},
author = {Hendro Wicaksono and Baris Yuce and Kris McGlinn and Ozum Calli},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Buildings and Semantics},
pages = {25},
publisher = {CRC Press},
keywords = {machine learning, ontologies, semantic web, smart cities, sustainability},
pubstate = {published},
tppubtype = {incollection}
}
2021
Wicaksono, Hendro
Accelerating Energy Transition to Green Electricity through Artificial Intelligence Journal Article
In: 2021.
BibTeX | Tags: artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability
@article{wicaksono2021accelerating,
title = {Accelerating Energy Transition to Green Electricity through Artificial Intelligence},
author = {Hendro Wicaksono},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
publisher = {OSF Preprints},
keywords = {artificial intelligence, energy management, machine learning, ontologies, semantic web, sustainability},
pubstate = {published},
tppubtype = {article}
}
Wicaksono, Hendro; Boroukhian, Tina; Bashyal, Atit
A demand-response system for sustainable manufacturing using linked data and machine learning Book Section
In: Dynamics in Logistics: Twenty-Five Years of Interdisciplinary Logistics Research in Bremen, Germany, pp. 155–181, Springer International Publishing Cham, 2021.
BibTeX | Tags: energy management, machine learning, ontologies, semantic web
@incollection{wicaksono2021demand,
title = {A demand-response system for sustainable manufacturing using linked data and machine learning},
author = {Hendro Wicaksono and Tina Boroukhian and Atit Bashyal},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Dynamics in Logistics: Twenty-Five Years of Interdisciplinary Logistics Research in Bremen, Germany},
pages = {155–181},
publisher = {Springer International Publishing Cham},
keywords = {energy management, machine learning, ontologies, semantic web},
pubstate = {published},
tppubtype = {incollection}
}