Publication

@article{nokey,

title = {Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study},

author = {Tamas Fekete and Girum Mengistu and Hendro Wicaksono },

doi = {https://doi.org/10.1016/j.scs.2025.106240},

year  = {2025},

date = {2025-03-15},

journal = {Sustainable Cities and Society},

volume = {122},

pages = {106240},

abstract = {The importance of developing sustainable urban transportation systems to protect the environment is increasingly recognized worldwide, particularly within the European Union. In the era of digitalization, data-driven approaches are crucial for informed decision-making. This study introduces a methodology leveraging causal artificial intelligence (causal AI) to uncover cause-and-effect relationships in urban transport data. Unlike traditional methods relying on correlations, causal AI identifies the true drivers of transport dynamics. A case study using MOL Bubi bike-sharing data from Budapest demonstrates how the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm revealed complex temporal dependencies within the data, with temperature emerging as the strongest causal factor positively influencing bike usage. Additionally, the reopening of the Chain Bridge led to a 10.7% increase in bike trips, as quantified by Causal Impact analysis. This case study can be extended to more complex scenarios with unpredictable outcomes. The insights gained provide policymakers with a deeper understanding, enabling them to design policies fostering sustainable urban mobility. These results showcase the potential of causal AI to guide policies that enhance sustainable urban mobility.},

keywords = {artificial intelligence, causal AI, machine learning, sustainability, transportation},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Artificial Intelligence in Open Innovation Project Management: A Systematic Literature Review on Technologies, Applications, and Integration Requirements},

author = {Moonita Limiany Prasetyo and Randall Aginta Peranginangin and Nada Martinovic and Mohammad Ichsan and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2199853124002397},

doi = {https://doi.org/10.1016/j.joitmc.2024.100445},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {Journal of Open Innovation: Technology, Market, and Complexity},

volume = {11},

number = {100445},

issue = {1},

abstract = {This study aims to provide insights to support organizations in building effective strategies for adopting Artificial Intelligence (AI) and improving project management processes. It focuses on open innovation projects. It employs a comprehensive and systematic literature review (SLR). A total of 365 publications have been chosen from a pool of 1265 papers in the IEEE and Scopus databases. The study develops a framework for literature synthesis guided by five research questions. Those questions address AI technologies, project management tasks, industries adopting AI, and requirements for successful adoption. The analysis reveals that Machine Learning is widely employed in project management, especially for predicting analytics, optimizing resources, and managing risks. AI improves open innovation project management by integrating diverse knowledge sources, enhancing collaboration, and providing strategic insights for decision-making. This study also found that AI adoption depends not only on technical infrastructure, integration with existing systems, and data readiness but also on leadership support, strategic alignment, financial resources, skills development, and organizational culture. The findings also highlight the importance of aligning AI initiatives with open innovation requirements, where collaboration, agility, and external knowledge integrations are crucial. The construction sector is at the forefront of adopting AI. This study fills a significant gap in previous research by identifying the technical and non-technical prerequisites for effectively incorporating AI into open innovation project management methodologies.},

keywords = {artificial intelligence, industry 4.0, innovation management, project management},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Digital Product Passport (DPP) technological advancement and adoption framework: A systematic literature review},

author = {Hendro Wicaksono and Abel Mengistu and Atit Bashyal and Tamas Fekete},

url = {https://www.sciencedirect.com/science/article/pii/S1877050925003655},

doi = {https://doi.org/10.1016/j.procs.2025.02.022},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {2980-2989},

abstract = {This research investigates the integration of Digital Product Passports (DPPs) into the Circular Economy (CE) paradigm. DPPs are digital papers that accompany products and contain detailed lifecycle data on materials, manufacturing processes, distribution networks, environmental effects, and end-of-life treatment. They improve industry openness, traceability, and sustainability by closing information gaps and encouraging sustainable product management. Despite the growing interest in DPPs, there is a significant gap in understanding the practical challenges and scalability of DPP adoption across the industry. This paper digs into technology developments and their adoptions for efficient DPP implementation within the CE framework through a systematic literature review (SLR). It investigates how DPPs promote resource efficiency, improve lifecycle assessments, and strengthen end-of-life management techniques. It also looks at the economic and legal consequences of integrating DPP into existing supply chains, stressing potential cost issues and the need for regulatory frameworks. The findings highlight DPPs’ significance in facilitating long-term product management decisions by providing openness and accountability across the product lifecycle. This paper also underlines the importance of stakeholder collaboration in realizing DPPs’ revolutionary potential for advancing the CE agenda. It proposes a conceptual model illustrating the technical architecture of DPPs, adoption framework, and DPP adoption ecosystem. Finally, this paper discusses the future research directions around DPPs based on the research gap identified through the SLR.

},

keywords = {artificial intelligence, digital product passport, technology adoption},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {From Theory to Practice: Implementing Causal AI in Manufacturing for Sustainability},

author = {Heonyoung Jeong and Tamas Fekete and Atit Bashyal and Hendro Wicaksono },

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002194},

doi = {https://doi.org/10.1016/j.procs.2025.01.211},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1495-1504},

abstract = {The use of AI in industry is increasingly popular, but its black-box nature poses decision-making challenges due to the lack of understanding of how variables influence each other. Causal AI addresses this by studying cause-and-effect relationships in the data. This paper explores applying causal AI in industry through a case study of CNC machines, which are significant in manufacturing and consume large amounts of energy. Industry 4.0 is transforming manufacturing, with CNC machines generating vast data analyzed by often opaque machine learning methods. Causal AI can uncover and quantify causal relationships between variables, aiding decision-making. Our case study uses CNC power consumption data to demonstrate causal AI in manufacturing, with existing models verifying our methodology. Future studies should extend our research to include variables without existing models, such as human habits. This case study serves as a starting point for other researchers, facilitating similar studies on complex data.},

keywords = {artificial intelligence, causal AI, causal inference, energy management, manufacturing, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Demand response based industrial energy management with focus on consumption of renewable energy: a deep reinforcement learning approach},

author = {Atit Bashyal and Hani Alnahas and Tina Boroukhian and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S1877050925002145},

doi = {https://doi.org/10.1016/j.procs.2025.01.206},

year  = {2025},

date = {2025-02-25},

journal = {Procedia Computer Science},

volume = {253},

pages = {1442-1451},

abstract = {Integrating Renewable Energy Resources (RESs) into power grids requires effective Demand Response (DR) programs. Despite high DR potential in industrial sectors, adoption lags behind that of residential and commercial sectors due to diverse operations and production continuity requirements. This paper explores a reinforcement learning (RL)-based DR scheme for energy-intensive industries, promoting the consumption of distributed Renewable Energy (RE) generation. Our approach introduces modifications to the existing Markov Decision Process (MDP) framework. It proposes a flexible reward structure that provides flexibility in balancing production requirements and promotes the consumption of RE. This study addresses the gap in industrial DR literature, emphasizing tailored DR solutions for industrial settings. The key highlight of our RL-based DR solution is its ability to facilitate a price-based DR scheme while promoting the integration of RE into the smart grid.

},

keywords = {artificial intelligence, demand response system, energy management, manufacturing, reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration},

author = {Agung Teguh Wibowo Almais and Adi Susilo and Agus Naba and Moechammad Sarosa and Alamsyah Muhammad Juwono and Cahyo Crysdian and Muhammad Aziz Muslim and Hendro Wicaksono},

url = {https://ieeexplore.ieee.org/abstract/document/10697160},

doi = {https://doi.org/10.1109/ACCESS.2024.3469637},

year  = {2024},

date = {2024-09-27},

urldate = {2024-09-27},

journal = {IEEE Access},

volume = {12},

abstract = {Objective: To determine the characteristics of a building after a natural disaster using image input through the integration of image analysis techniques. Methods: Several image analysis techniques, including GLCM and PCA, were employed. The GLCM process converts image input into numerical values using 8 different angles and pixel distances of 1 and 0.5 pixels. The numerical values from GLCM are then processed by PCA to extract information stored in the images of buildings post-disaster. Results: The PCA process revealed different information between images processed with GLCM at 1 pixel distance and those at 0.5 pixel distance. Validation by surveyors confirmed that the accurate information corresponding to real images was obtained from GLCM with a 0.5 pixel distance, indicating severe damage. The PCA results using GLCM at 0.5 pixel distance produced 2D and 3D visualizations with dominant coordinates in the severely damaged cluster, with a value range (n) of n ≥ 2. Conclusion: Based on these findings, the integration of image analysis techniques, specifically GLCM and PCA, can be used to determine the level of damage to buildings after a natural disaster.},

keywords = {artificial intelligence, data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@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  = {2024},

date = {2024-03-22},

urldate = {2024-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

pages = {1–25},

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, demand response system, energy management, machine learning, manufacturing, ontologies, reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

@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}

}