Publications and Talks

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}