Computational Intelligence Techniques for Sustainable Supply Chain Management

1st Edition - May 23, 2024
Editors: Sanjoy Kumar Paul, Sandeep Kautish
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 4 6 4 - 2
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 4 6 5 - 9

Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain su… Read more

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Computational Intelligence Techniques for Sustainable Supply Chain Management presents state-of-the-art computational intelligence techniques and applications for supply chain sustainability issues and logistic problems, filling the gap between general textbooks on sustainable supply chain management and more specialized literature dealing with methods for computational intelligence techniques. This book focuses on addressing problems in advanced topics in the sustainable supply chain and will appeal to practitioners, managers, researchers, students, and professionals interested in sustainable logistics, procurement, manufacturing, inventory and production management, scheduling, transportation, and supply chain network design.

Cover image
Title page
Table of Contents
Copyright
List of contributors
About the editors
Preface
Acknowledgments
Chapter one. A review of computational tools, techniques, and methods for sustainable supply chains
Abstract
1.1 Introduction
1.2 A brief description of sustainable supply chains
1.3 Computational intelligence for sustainable supply chain management
1.4 Computational tools, techniques, and methods for sustainable supply chains
1.5 Conclusions and research scopes
References
Chapter two. Big data analytics in construction: laying the groundwork for improved project outcomes
Abstract
2.1 Introduction
2.2 Literature review
2.3 Methodology
2.4 Results
2.5 Discussion
2.6 Managerial implications
2.7 Conclusion, limitations, and future scopes
References
Chapter three. Role of unmanned air vehicles in sustainable supply chain: queuing theory and ant colony optimization approach
Abstract
3.1 Introduction
3.2 Literature review
3.3 Methodology
3.4 Results and discussion
3.5 Conclusion and recommendations
References
Chapter four. Exploring the enablers of sustainable supply chain management: A simplified computational intelligence perspective
Abstract
4.1 Introduction
4.2 Methodology
4.3 Findings
4.4 Conclusion, implications, and limitations
References
Further reading
Chapter five. Scenario analysis for long-term planning: stratified decision-making models in sustainable supply chains
Abstract
5.1 Introduction
5.2 Key variables and definitions
5.3 The stratified decision-making model
5.4 How has the model been used?
5.5 Application in sustainable supply chain management
5.6 Managerial implications and future research opportunities
5.7 Conclusion
References
Chapter six. Machine learning techniques for sustainable industrial process control
Abstract
6.1 Introduction
6.2 Industrial process control
6.3 Machine learning in industrial process control
6.4 Role of machine learning in manufacturing sustainability
6.5 Anomaly detection
6.6 Anomaly detection and root cause identification in a hydropower dataset (a case study)
6.7 Problem description
6.8 Conclusion
References
Chapter seven. Intelligent sustainable infrastructure for procurement and distribution
Abstract
7.1 Introduction
7.2 Participants in the infrastructure workflow
7.3 Case study
7.4 Conclusion
7.5 Future scope
References
Chapter eight. Machine learning techniques for route optimizations and logistics management description
Abstract
8.1 Introduction
8.2 Development in logistics
8.3 Applications of artificial intelligence in transport
8.4 Benefits of artificial intelligence usage in the logistics industry
8.5 Role of artificial intelligence in route planning and route optimization
8.6 Machine learning and internet of things in smart transportation
8.7 Machine learning in logistics—warehouse management
8.8 Supply chain planning using machine learning
8.9 Objectives and main specificities of reinforcement learning-based routing protocols
8.10 Path planning strategies
8.11 Path planning and obstacle avoidance algorithms
8.12 Operations with route optimization
8.13 Vehicle routing problem and delivery optimization
8.14 Optimization of delivery operations
8.15 Last-mile delivery efficiency
8.16 Conclusion
References
Chapter nine. Revolutionizing sustainability: the role of robotics in supply chains
Abstract
9.1 Introduction
9.2 Methodological framework for supply chain automation
9.3 Supply chains in different sectors
9.4 Automation in supply chain
9.5 Predictive analytics
9.6 Role of robots in supply chain automation
9.7 Benefits of supply chain automation
9.8 Challenges and future directions
9.9 Sustainable supply chains
9.10 Case studies
9.11 Emerging trends and research gaps
9.12 Conclusion and future scope
References
Chapter ten. Computational techniques for sustainable green procurement and production
Abstract
10.1 Introduction
10.2 Real-life examples
10.3 Meaning and background
10.4 Literature review
10.5 Challenges
10.6 Proposed solution
10.7 Findings and conclusion
10.8 Outlook and future research
List of abbreviations
References
Chapter eleven. Predictive big data analytics for supply chain demand forecasting
Abstract
11.1 Introduction
11.2 Deep learning-based forecasting approaches
11.3 The proposed switching-based forecasting approach with neural networks
11.4 The considered supply chain model
11.5 Experimental design for the proposed switching-based forecasting approach with neural networks
11.6 Result analyses for the proposed switching-based forecasting approach with neural networks
11.7 Conclusion
References
Chapter twelve. Bayesian network based on cross bow-tie to analyze differential effects of internal and external risks on sustainable supply chain
Abstract
12.1 Introduction and motivation
12.2 Literature review and state of the art
12.3 Application definition
12.4 Proposed solution
12.5 Analysis
12.6 Discussion
12.7 Conclusion
Appendix 12.A
References
Chapter thirteen. Streamlining supply chain operations: A case study of bigbasket.com
Abstract
13.1 Introduction and overview of the e-commerce market in India
13.2 The grocery industry in India
13.3 About Bigbasket.com
13.4 Business model of Bigbasket.com
13.5 Supply chain management at Bigbasket
13.6 IT in supply chain management at Bigbasket
13.7 Bigbasket’s supply chain management challenges
13.8 Personal interview with the expert at Bigbasket.com
13.9 Way forward for Bigbasket.com
13.10 Conclusion
Appendix 13.1
Appendix 13.2
Appendix 13.3
Appendix 13.4
Appendix 13.5
References
Chapter fourteen. Applications of artificial intelligence in Echo Global Logistics
Abstract
14.1 Introduction
14.2 Echo global logistics
14.3 Artificial intelligence and its role in supply chain transformation
14.4 Challenges
14.5 Research methodology
14.6 Proposed solution
14.7 Findings and conclusion
References
Index

Sanjoy Kumar Paul

Sanjoy Kumar Paul, PhD is an Associate Professor in operations and supply chain management at the University of Technology Sydney (UTS), Sydney, Australia. He has published more than 140 articles in top-tier journals. He is also an associate editor, area editor, editorial board member, and active reviewer of several reputed journals. Dr. Paul has received several awards, including the ASOR Rising Star Award from the Australian Society for Operations Research, the Excellence in Early Career Research Award from the UTS Business School, and the Stephen Fester Prize for most outstanding thesis from UNSW. Based on his citation records in 2020, 2021 and 2022, he was included in the top 2% of scientists in author databases of standardized citation indicators. His research interests include sustainable supply chain management, supply chain resilience, applied operations research, modeling and simulation, and intelligent decision-making.

Affiliations and expertise

Associate Professor, UTS Business School, University of Technology Sydney, Sydney, Australia

Sandeep Kautish

Sandeep Kautish, PhD is Professor and Director at Apex Institute of Technology (AIT-CSE), Chandigarh University, Punjab India and an academician by choice and has more than 20 years of full-time experience in teaching and research. He has been associated with Asia Pacific University Malaysia for over five years at their TNE site at Kathmandu Nepal in the capacity of Director-Academics. He earned his doctorate degree in Computer Science on Intelligent Systems in Social Networks. He has over 100 publications and his research works have been published in highly reputed journals, i.e., IEEE Transaction of Industrial Informatics, IEEE Access, and Multimedia Tools and Applications, etc. Dr. Kautish has edited 24 books with leading publishers, i.e., Elsevier, Springer, Emerald, and IGI Global, and is an editorial member/reviewer of various reputed journals. His research interests include healthcare analytics, business analytics, machine learning, data mining, and information systems.

Affiliations and expertise

Professor and Director, Apex Institute of Technology (AIT - CSE), Chandigarh University, Punjab, India

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