Ali / Goodarzian / Sankaranarayanan | Sustainable Supply Chain Management | Buch | 978-0-443-33807-6 | sack.de

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm

Ali / Goodarzian / Sankaranarayanan

Sustainable Supply Chain Management

Computational Approaches and Modelling
Erscheinungsjahr 2025
ISBN: 978-0-443-33807-6
Verlag: Elsevier Science

Computational Approaches and Modelling

Buch, Englisch, 300 Seiten, Format (B × H): 191 mm x 235 mm

ISBN: 978-0-443-33807-6
Verlag: Elsevier Science


Sustainable Supply Chain Management: Computational Approaches and Modelling provides a comprehensive understanding of how computational approaches can be leveraged to address the challenges associated with sustainable supply chain management. It offers insights into emerging and advanced modelling techniques to optimize supply chain operations while considering environmental, social, and economic factors. This book will be a valuable resource for researchers, practitioners, and policymakers interested in understanding and implementing sustainable supply chain management practices through computational approaches and modelling. This book helps researchers understand the opportunities, challenges, and solutions by developing innovative computational tools and techniques to integrate sustainability considerations into supply chain decision-making processes.

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Weitere Infos & Material


Part I: Introduction to Sustainable Supply Chain Management
1. Overview of Sustainable Supply Chain Management

Part II: Computational Approaches for Sustainable Supply Chain Management
2. Big Data Analytics for Sustainable Supply Chains
3. Advancements in Simulation and Hybrid Models for Sustainable and Resilient Supply Chains: A Multi-Criteria Decision-Making Perspective
4. Optimization Models for Sustainable Supply Chain Design
5. Green Transportation and Logistics Optimization

Part III: Supply Chain Management through Computational Modelling
6. Risk Assessment and Management in Supply Chains
7. Demand Forecasting and Inventory Management for Supply Chains Sustainability
8. Real-Time Decision-Making and Adaptability in Supply Chains

Part IV: Case Studies and Future Directions
9. Case Studies in Sustainable Supply Chain Management
10. Future Directions and Emerging Trends in Computational Approaches


Ali, Irfan
Dr. Irfan Ali received B.Sc., M.Sc., M.Phil., and Ph.D. degrees from Aligarh Muslim University. He is currently working as a Senior Assistant Professor with the Department of Statistics and Operations Research at Aligarh Muslim University. He received the Post Graduate Merit Scholarship Award during his M.Sc. (statistics) and the UGC-BSR Scholarship awarded during his Ph.D. (statistics) programs. His research interests include Applied statistics, supply chain networks and management, mathematical programming, Fuzzy logic & optimization, multi-objective optimization, UN Sustainable Development Goals and Renewable Energy Production Modelling. He has published more than 100+ research articles in reputed journals and serves as a Reviewer for several journals. He has published several edited books for Springer and Taylor's Francis, CRC press.

Sankaranarayanan, Bathrinath
Dr. Bathrinath Sankaranarayanan is an Associate Professor in the Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India. His current area of research includes Industry 4.0, Supply Chain Management, Circular Economy, Risk Assessment, Multi-Criteria Decision Making, Scheduling, Optimization of process parameters, Soft Computing and Artificial intelligence. He has obtained his Post-Doctoral Fellowship in University of Southern Denmark, Odense, Denmark. He has also obtained his Young Scientist Fellowship from Tamilnadu State Council for Science and Technology (TNSCST). He has published 80 papers in journal of national/international repute and presented a number of papers in various conferences/symposia in India and abroad. He is presently guiding a number of master/doctoral research scholars.

Goodarzian, Fariba
Dr. Fariba Goodarzian is a Research Associate at the Edinburgh Business School, Heriot-Watt University, United Kingdom. She passed a Postdoctoral Researcher at the University of Tehran, Department of Industrial Engineering, Iran, in Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, United States, and at the University of Sevilla in Spain. She earned her Ph. D. degree in Industrial Engineering from Yazd University, Iran (2017-2020), M. Sc. degree in Industrial Engineering from the University of Science and Technology of Mazandaran, Behshar (2014-2016). She graduated with a B. Sc. degree in Industrial Engineering from Mazandaran University of Science and Technology, Babol (2010-2014). Some of her publications include, Applied Soft Computing; Applied Mathematical Modeling; Engineering Applications and Artificial Intelligence; Journal of Computational Design and Engineering; Computers and Industrial Engineering. Her main research interests are in Supply Chain Management, Health Care Management, Operations Research, Network Design and Uncertainty Programming.

Ali, Syed Mithun
Dr. Syed Mithun Ali is a Professor for the Department of Industrial and Production Engineering at the Bangladesh University of Engineering and Technology. He was awarded the Forum 86 Research Excellence Award' 2021 for his contributions in the field of sustainable supply chains. He has published more than 100 articles in top-tier journals including the Journal of Cleaner Production, International Journal of Production Economics, Journal of Retailing and Consumer Services, International Journal of Production Research, Sustainable Production and Consumption, Industrial Management and Data Systems, Computers and Industrial Engineering, Annals of Operations Research, Energy, Applied soft Computing, Business Strategy and the Environment, Expert Systems with Applications and so on. His research interest includes sustainable, circular and digital supply chains, decision analytics, and applications of machine learning in industrial decision making.



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