Optimization and Data Science in Supply Chain and Logistics

Towards Operational and Business Excellence
Erscheinungsjahr 2026
ISBN: 978-981-9206-33-9
Verlag: Springer Verlag, Singapore

Buch, Englisch, 263 Seiten, Format (B × H): 155 mm x 235 mm

This book explores how optimization and data science significantly impact the operations of supply chain and logistics and investigates them extensively for additional benefits. It demonstrates the concept and implementation of data science methods, advanced analytics, optimization, metaheuristics, and machine learning algorithms developed over the years for critical decision-making in warehousing, inventory, and transportation in supply chain management that facilitates the optimization of supply chain and logistics operations. It covers the following topics with an emphasis on their applications in areas like supply chain coordination, pricing and revenue management, warehousing, reverse logistics, facility location and supply chain network design, transportation and routing. The book is a great resource for senior undergraduate students intending to get themselves acquainted with interesting future trends in the topic, postgraduate students curious to augment their knowledge in the detailed theory and applications of the topic, PhD and postdoctoral students who want to focus in-depth on the stimulating research areas on these issues, and practitioners from industry who wish to explore fruitful applications and their future trends in supply chain and logistics.

Zielgruppe

Research

Autoren/Hrsg.

Jha, Jitendra Kumar

Mukherjee, Indrajit

Sengupta, Raghu Nandan

Basu, Bhaskar

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Einkauf, Logistik, Supply-Chain-Management

Inhaltsverzeichnis

Chapter 1 Optimization and Data Science in Supply Chain and Logistics: Towards Operational and Business Excellence.- Chapter 2 Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows.- Chapter 3 Understanding the Capability of Emerging Technologies in Supply Chain Management.- Chapter 4 A Deep Reinforcement Learning Framework for Supply Chain Inventory Management.- Chapter 5 Optimisation and Data Science in Supply Chain using the Taguchi Method.- Chapter 6 Optimizing End-of-Life Product Recovery: Leveraging IoT and Machine Learning in the Reverse Supply Chain.- Chapter 7 Real-Time Order Monitoring and Ai-Driven Analytics in Controlled Substances Order Management.- Chapter 8 Integrated Lot-Sizing and Warehouse Pooling Problem for Horizontal Collaboration.- Chapter 9 Establishing Supply Chain Management 4.0 Maturity Level and Assessing Technologies for Pharmaceutical Industries: Industry 4.0 perspective.- Chapter 10 Addressing Challenges in Dengue Medical Supply and Navigating Nonlinear Equations using Computer Algorithms.- Chapter 11 Analysis of Price Differentiation and Greening Decisions in a Dual-Channel Supply Chain.

Über Autor(innen)

Jitendra Kumar Jha  is working as a Professor in the Department of Industrial and Systems Engineering at IIT Kharagpur. He obtained his PhD from IIT Kanpur. He has received several scholarships and awards from DRPG IIT Kanpur, BITSAA of NorthAmerica, SJ Jindal Trust New Delhi, IIT Kharagpur. His main areas of teaching and research include operations research, statistical decision modeling, facility planning, supply chain and logistics planning, and inventory control. He has published/presented more than sixty papers in international journals and conferences, and his publications have appeared in the reputed journals like  ,  ,  ,  ,  , and other leading journals of Industrial Engineering. He is serving as an editorial board member of  .

Indrajit Mukherjee  is Professor at Shailesh J. Mehta School of Management, IIT Bombay. He did his Ph.D. from the Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur. He did his Master's degree in Quality, Reliability & Operations Research from Indian Statistical Institute, Kolkata, India. He has authored and co-authored several research papers published in refereed and reputed journals. His research contributions are published in the ,    ,  ,  ,  . His primary research interest is in multivariate quality control, quality management, applied operations research, and sourcing in the supply chain.

Raghu Nandan Sengupta  is Professor, Department of Industrial & Management Engineering, Indian Institute of Technology Kanpur. He obtained his PhD from Indian Institute of Management Calcutta and PDF from Princeton University, USA. His research interests are in areas of sequential estimation, statistical and mathematical reliability theory, risk analysis, optimization techniques in finance, meta heuristic techniques, reliability based optimization, robust optimization. His research work has been published in  ,  ,  ,  ,  ,  ,  ,  ,  . He has also edited two books titled (i) Decision Sciences: Theory and Practice, (2016); and (ii) Studies in Quantitative Decision Making (2022), the latter being published by Springer.

Bhaskar Basu  is Professor of Information Systems at Xavier Institute of Management, XIM University, Bhubaneswar. He obtained his PhD from Indian Institute of Technology, Kharagpur (India) and is a University Gold Medallist (Jadavpur University, India). He also has a Post Graduate Diploma in Business Management (equivalent to MBA) from Indian Institute of Management, Calcutta. His research interests are in the areas of knowledge management, AI applications in business and sports management. His research work has been published in journals like VINE,  , IJITDM etc. He has also edited a book titled “Organizational Learning-Perspectives and Practices” (2006) and another one on sports management (co-edited) with Springer (2023).