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Dr. Fabio Sgarbossa is a Full Professor of Industrial Logistics at NTNU's Department of Mechanical and Industrial Engineering (MTP), leading the Production Management Research Group and overseeing the Logistics 4.0 Lab. He holds a PhD in Industrial Engineering from the University of Padova (2010) and previously served as an Associate Professor there. His research focuses on industrial logistics design, supply chain management, digitalization in logistics, and human factors in production systems, with contributions to over 150 international publications. He is an Associate Editor for the International Journal of Production Research and actively involved in organizing international conferences. His work integrates advanced technologies like Industry 4.0, additive manufacturing, and AI to address challenges in supply chain resilience, sustainable production, and ergonomic system design. Notable projects include optimizing warehouse systems, evaluating assistive technologies for worker well-being, and developing decision frameworks for spare parts management. His research bridges theoretical models with practical applications, emphasizing human-centered design and circular economy principles. Dr. Sgarbossa’s contributions extend to strategic frameworks for disassembly systems, collaborative logistics in rural contexts, and the mitigation of Lean implementation barriers through digitalization. His Logistics 4.0 Lab serves as a platform for experimental research and education, fostering innovation in smart manufacturing and logistics solutions. He collaborates extensively on EU-funded projects and guides industry partnerships to translate academic insights into actionable strategies.

• Supply Chain Management
• Industrial Logistics
• Digitalization in Logistics
• +5 more

Dr. Samira Keivanpour is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. She holds a Ph.D. in Industrial Engineering from Laval University, an MBA in Operation Management from Iran, and a B.Sc. in Electrical Engineering from Iran. Prior to joining Polytechnique Montréal, she was a faculty member at Thompson Rivers University and completed her postdoctoral fellowship at Laval University's Department of Mechanical Engineering (2015-2017). Her research focuses on Circular Economy & End-of-Life Product Management, Sustainable & Circular Supply Chains, Human-Robot Collaboration & Smart Automation in Logistics, and AI for Sustainable Operations. Her work bridges theoretical frameworks with practical applications in sustainable manufacturing and logistics. Her recent publications demonstrate a strong trend toward integrating artificial intelligence, particularly fuzzy logic and reinforcement learning approaches, with sustainable operations management. Her work spans multiple domains including circular supply chains, human-robot collaboration, and sustainable manufacturing systems, with particular emphasis on aerospace applications and end-of-life product management. Dr. Keivanpour actively supervises graduate students, with eight completed theses (one Ph.D. and seven Master's) between 2021-2024, focusing on sustainable manufacturing, circular economy, and smart logistics applications. She holds significant research affiliations as a Co-investigator with the Safran Industrial Chair on Multifunctional Passive Acoustic Treatments for Turbofan Composite Structures (TAPPIS) and the Institutional Research Chair on Additive Manufacturing technologies (FACMO 2), and is a member of both the Research Group in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT).

• Circular Economy
• End-of-Life Product Management
• Sustainable Supply Chains
• +7 more

Burak Gökgür is an Assistant Professor of Operations Management at the Business School of Sabancı University, located in Tuzla, İstanbul, Turkey. His research focuses on operations management, pricing strategies, production systems optimization, scheduling algorithms, constraint programming, and heuristic approaches. He holds a Ph.D. and has published extensively in journals like OR Spectrum, International Journal of Production Research, and European Journal of Operational Research. His work addresses complex operational challenges such as dynamic pricing mechanisms, inventory control in fluctuating environments, and optimization of manufacturing systems. Teaching includes courses like MGMT 201 (Introduction to Management) and OPIM 611 (Modeling in Operations Management). No scientific awards or grants are explicitly mentioned in the provided materials. He advises no listed students, and no lab affiliations are noted.

• Operations Management
• Pricing Strategies
• Production Systems Optimization
• +5 more

Alexandre Dolgui is Distinguished Professor and Head of the Department of Automation, Production and Computer Sciences at IMT Atlantique in Nantes, France. He serves as Editor-in-Chief of the International Journal of Production Research and has established himself as a leading researcher in production and supply chain optimization. His academic career spans multiple institutions including Jean Monnet University and University of Lorraine Mines Nancy, with a visiting professorship at the Chinese Academy of Sciences. PhD in Industrial Engineering, University of Technology of Compiègne (2000) Doctorate in Computer Science and Control, National Academy of Sciences of Belarus (1990) Bachelor in Automated Data Processing and Management Systems, Belarusian State University of Informatics and Radioelectronics (1986) Dolgui's research primarily focuses on manufacturing line design, production planning and scheduling, and supply chain optimization. His work bridges theoretical operations research with practical industrial applications, particularly in the context of Industry 4.0 and sustainable manufacturing. He has pioneered approaches to address challenges in assembly line balancing, reconfigurable manufacturing systems, and resilient supply chain design under uncertainty. His research integrates mathematical programming, optimization techniques, and increasingly, artificial intelligence methodologies to solve complex production and logistics problems. An analysis of his recent publications reveals a strong emphasis on supply chain resilience, particularly regarding the ripple effect of disruptions, digital supply chain twins, and sustainable manufacturing practices. His work increasingly incorporates AI and machine learning techniques while maintaining strong theoretical foundations in operations research. The interdisciplinary nature of his research connects industrial engineering with computer science, economics, and environmental sustainability. Highly Cited Researcher 2022, 2023, and 2024 by Clarivate Best Paper Award for 2023 by Omega Fellow of IISE (Institute of Industrial and Systems Engineers) As Editor-in-Chief of the International Journal of Production Research, Dolgui has significantly influenced the field's research direction and quality standards. His leadership extends to organizing major conferences including the IFAC Symposium on Information Control Problems in Manufacturing (INCOM). His research group maintains strong international collaborations, particularly with researchers from China, Russia, and other European institutions, securing funding for projects addressing contemporary manufacturing and supply chain challenges. Dolgui leads the Alexandre Dolgui Lab at IMT Atlantique, which focuses on production systems engineering and supply chain management. The lab maintains strong industry partnerships and participates in European research networks addressing advanced manufacturing challenges. Current research directions include Industry 5.0 applications, human-robot collaborative systems, and sustainable supply chain design, reflecting the lab's commitment to addressing both theoretical and practical challenges in modern production environments.

• Operations Management
• Logistics
• Supply Chain Optimization
• +4 more

Sanchoy Das is a Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on manufacturing systems, supply chain engineering, warehouse optimization, and design for manufacturability. He has led federal grants including projects on flexible manufacturing methodologies and product design quality. His work integrates industrial engineering principles with emerging technologies like machine learning for metabolic rate prediction in firefighting scenarios. Key research areas include e-commerce fulfillment strategies, warehouse layout optimization, and explosive storage policies. He has published extensively on topics such as order picking heuristics, robotic task repeatability, and healthcare resource allocation. Das has collaborated on projects funded by the National Science Foundation, addressing challenges in traditional factory processes and product disassembly. Media engagements highlight his expertise in retail logistics, port strike impacts, and supply chain analytics. His academic contributions span over 66 peer-reviewed articles, with a focus on operational efficiency in manufacturing and service systems. Das teaches advanced courses including Design for Manufacturability and Supply Chain Engineering at NJIT.

• Industrial Engineering
• Operations Research
• Manufacturing Systems
• +5 more

Ying Tang is a Professor and Associate Chair in the Department of Electrical & Computer Engineering at Rowan University , where she also serves as Undergraduate Program Coordinator. Her academic journey includes a Ph.D. in Electrical Engineering from New Jersey Institute of Technology, an M.S. in Computer Engineering, and a B.S. in Electrical Engineering, both from Northeastern University. Research Interests focus on: Design, control, and optimization of Discrete Event Systems Intelligent systems for Health and Computational Health Sustainable Production Automation and Digital Engineering Machine Learning and Blockchain applications Energy-aware Manufacturing and AI-driven Education Recent Research Trends show emphasis on: Sustainable manufacturing optimization using harmony search algorithms Machining process modeling with energy consumption analysis AI-integrated educational games for personalized learning Error coupling prediction in precision gear manufacturing Human-machine interaction in industrial systems Scientific Contributions include: Co-inventor of U.S. Patent 9,626,568 for spatial structured light applications Founding chair of two IEEE Technical Committees Over 200 publications with 4420+ citations (h-index 37) Academic Leadership involves: Associate Editor roles in multiple IEEE journals Guest Editor for special issues on Cyber-physical Social Intelligence Editorial Board Member of International Journal of Remanufacturing Research Funding comes from NSF , EPA , DOT , private foundations, and industry partners. Major projects include: SPARC (Systematic Problem-solving & Algorithmic Reasoning for Children) PING (Personalized Instruction and Need-aware Gaming) E4SPA (Sustainable Production Automation Education) Collaborative VR Learning Environment

• Discrete Event Systems
• Sustainable Production and Service Automation
• Intelligent Game-based Learning Environments
• +7 more

János Abonyi is a Professor at the Department of Process Engineering, Faculty of Engineering, University of Pannonia, Hungary. With over 118 publications spanning from 2000 to 2024, he has established himself as a leading researcher in process systems engineering, machine learning applications, and Industry 4.0 technologies. His work bridges theoretical computational intelligence with practical industrial applications, particularly in process optimization, fault detection, and smart manufacturing systems. Abonyi's research interests focus on applying machine learning and data mining techniques to industrial process systems. His work spans several key areas including soft sensor development, fault detection and diagnosis, optimization algorithms for manufacturing, and Industry 4.0/5.0 applications. He has pioneered approaches combining sequence mining with deep learning for alarm management, reinforcement learning for disassembly line optimization, and explainable AI for industrial applications. His research demonstrates a consistent trajectory from fundamental computational intelligence methods to their practical implementation in real industrial settings. His recent publications (2020-2024) show a strong emphasis on Industry 4.0 and 5.0 applications, with particular focus on human-machine collaboration, real-time locating systems, digital twins, and explainable AI for industrial applications. The interdisciplinary nature of his work is evident through collaborations across computer science, electrical engineering, and chemical engineering domains. His research group has produced significant contributions in optimization algorithms, particularly in reinforcement learning applications for manufacturing and process systems. Abonyi has supervised numerous PhD students who have become active researchers in their own right, including Tamás Ruppert, Ágnes Vathy-Fogarassy, and Balazs Feil. His collaborative network extends internationally, with publications in top journals including IEEE Access, Sensors, and Computers & Chemical Engineering. His work demonstrates strong industry relevance with practical implementations in manufacturing, process control, and industrial automation contexts.

• Machine Learning
• Process Systems Engineering
• Industry 4.0
• +5 more

Oumayma LAOUINI is a Researcher affiliated with the Université de Technologie de Compiègne (UTC). Her work focuses on operations research and industrial engineering, particularly in disassembly line balancing, stochastic programming, and uncertainty management in manufacturing systems. She holds a Doctorate (PhD) and has contributed to advanced methodologies in disassembly optimization under yield uncertainty and robotic integration. Her research addresses challenges in production systems through exact and stochastic approaches, with recent contributions emphasizing collaborative robotics in disassembly processes and capacitated lot-sizing under random yield conditions. Key areas of expertise include bi-objective optimization, decision-making under uncertainty, and reverse logistics. Dr. LAOUINI’s publications reflect a strong emphasis on bridging theoretical models with practical industrial applications, particularly in enhancing efficiency and adaptability in manufacturing and disassembly operations.

• Operations Research
• Industrial Engineering
• Disassembly Line Balancing
• +3 more

Fabio Sgarbossa is a Professor of Industrial Logistics at the Norwegian University of Science and Technology (NTNU), based in the Department of Mechanical and Industrial Engineering within the Faculty of Engineering. He holds a Master’s in Management Engineering (2005) and a PhD in Industrial Engineering (2010) from the University of Padova, Italy. His research focuses on design and management of industrial logistics systems, supply chain optimization, digitalization in logistics, materials handling, maintenance strategies, and human factors in production systems. He leads the Lab Logistics 4.0 at NTNU and is actively involved in European and national research projects. He serves as an Associate Editor for the International Journal of Production Research and contributes to editorial boards of high-impact journals like the International Journal of Production Economics . Key areas of expertise include production management, supply chain viability, Industry 4.0 technologies (e.g., autonomous mobile robots, additive manufacturing), and human-centric system design. He has authored/co-authored over 130 publications, with an h-index of 31 (Scopus, 2022). Recent work explores topics such as renewable hydrogen supply chains, assistive technologies for assembly tasks, and collaborative logistics in rural contexts. His research integrates theoretical frameworks with practical applications, addressing challenges in smart manufacturing, circular economy, and resilience in supply chains. He collaborates with industry partners to translate academic insights into actionable solutions for logistics and production systems.

• Industrial Logistics
• Supply Chain Management
• Digitalization in Logistics
• +5 more

Professor Eren Ozceylan is a faculty member at Gaziantep University, Faculty of Engineering, Industrial Engineering Department . He holds a PhD in Computer Engineering from Selcuk University (2013), an MSc in Industrial Engineering (2010), and a BSc in Industrial Engineering (2007). His research focuses span Supply Chain Management , Logistics and Transportation , Modeling and Optimization , and Humanitarian Logistics , with a strong emphasis on GIS-based spatial analysis , Fuzzy Logic , and Industry 4.0 technologies. Education: PhD (2013) in Computer Engineering, Selcuk University MSc (2010) in Industrial Engineering, Selcuk University BSc (2007) in Industrial Engineering, Selcuk University His work integrates Multicriteria Decision Analysis (MCDM) with Mathematical Programming , addressing challenges in Sustainable Supply Chains , Refugee Camp Location Planning , and Smart Manufacturing . Recent publications highlight applications of Blockchain for supplier selection, AI-assisted translation in logistics, and Drone-based inventory management . He has authored 25 book chapters and 125 peer-reviewed journal articles, often collaborating with researchers like Ibrahim Mirac Eliguzel, Suleyman Mete, and Cihan Cetinkaya. Scientific contributions include pioneering studies on Disassembly Line Balancing for end-of-life products, GIS-based Maximum Coverage Models for humanitarian depots, and Fuzzy AHP-WASPAS frameworks for electric vehicle charging stations. His work appears in high-impact journals such as Expert Systems with Applications , IEEE Transactions on Engineering Management , and Journal of Cleaner Production , with a focus on Q1/Q2 quartile journals and SCI/SSCI indexing . Professor Ozceylan's methodological toolkit includes Hybrid Metaheuristics (e.g., Discrete Crow Search, Beam Search), Entropy Weighting , and Spherical Fuzzy Sets . He has presented at 218 international/national conferences, addressing topics like Autonomous Systems , ERP Integration for Industry 4.0 , and AI in Last-Mile Delivery . His research bridges theoretical innovation with real-world applications in sectors ranging from Textiles to Public Transportation in Turkey-Syria border regions.

• Industrial Engineering
• Supply Chain Management
• Logistics and Transportation
• +9 more