Sustainable supply chain management involves integrating environmentally and financially viable practices into the complete supply chain lifecycle, from product design and development to material selection and sourcing, manufacturing, packaging, transportation, and distribution. A sustainable supply chain ensures balance between economic, social, and environmental performances – such as better assurance of human rights, ethical work practices, carbon footprint reduction, waste management, and resource efficiency.
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. This book focuses on addressing problems in advanced topics in the sustainable supply chain, and will appeal to practitioners, managers, researchers, academicians, students, and professionals interested in sustainable logistics, sustainable procurement, sustainable manufacturing, sustainable inventory and production management, sustainable scheduling, sustainable transportation, and sustainable network design.
1. Computational Intelligence Techniques for Sustainable Supply Chain Management - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. About the editors - p. Page vii 9. Preface - p. Page viii 10. Acknowledgments - p. Page xi 11. List of Illustrations - p. Page xiii 12. List of Tables - p. Page xiv 13. Chapter one : A review of computational tools, techniques, and methods for sustainable supply chains - p. Page xiv 14. Chapter two : Big data analytics in construction: laying the groundwork for improved project outcomes - p. Page 18 15. Chapter three : Role of unmanned air vehicles in sustainable supply chain: queuing theory and ant colony optimization approach - p. Page 51 16. Chapter four : Exploring the enablers of sustainable supply chain management: A simplified computational intelligence perspective - p. Page 82 17. Chapter five : Scenario analysis for long-term planning: stratified decision-making models in sustainable supply chains - p. Page 107 18. Chapter six : Machine learning techniques for sustainable industrial process control - p. Page 136 19. Chapter seven : Intelligent sustainable infrastructure for procurement and distribution - p. Page 172 20. Chapter eight : Machine learning techniques for route optimizations and logistics management description - p. Page 194 21. Chapter nine : Revolutionizing sustainability: the role of robotics in supply chains - p. Page 223 22. Chapter ten : Computational techniques for sustainable green procurement and production - p. Page 268 23. Chapter eleven : Predictive big data analytics for supply chain demand forecasting - p. Page 296 24. Chapter twelve : Bayesian network based on cross bow-tie to analyze differential effects of internal and external risks on sustainable supply chain - p. Page 328 25. Chapter thirteen : Streamlining supply chain operations: A case study of bigbasket.com - p. Page 378 26. Chapter fourteen : Applications of artificial intelligence in Echo Global Logistics - p. Page 402 27. Index - p. Page 427
Sustainable supply chain management involves integrating environmentally and financially viable practices into the complete supply chain lifecycle, from product design and development to material selection and sourcing, manufacturing, packaging, transportation, and distribution. A sustainable supply chain ensures balance between economic, social, and environmental performances – such as better assurance of human rights, ethical work practices, carbon footprint reduction, waste management, and resource efficiency.
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. This book focuses on addressing problems in advanced topics in the sustainable supply chain, and will appeal to practitioners, managers, researchers, academicians, students, and professionals interested in sustainable logistics, sustainable procurement, sustainable manufacturing, sustainable inventory and production management, sustainable scheduling, sustainable transportation, and sustainable network design.
1. Computational Intelligence Techniques for Sustainable Supply Chain Management - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. About the editors - p. Page vii 9. Preface - p. Page viii 10. Acknowledgments - p. Page xi 11. List of Illustrations - p. Page xiii 12. List of Tables - p. Page xiv 13. Chapter one : A review of computational tools, techniques, and methods for sustainable supply chains - p. Page xiv 14. Chapter two : Big data analytics in construction: laying the groundwork for improved project outcomes - p. Page 18 15. Chapter three : Role of unmanned air vehicles in sustainable supply chain: queuing theory and ant colony optimization approach - p. Page 51 16. Chapter four : Exploring the enablers of sustainable supply chain management: A simplified computational intelligence perspective - p. Page 82 17. Chapter five : Scenario analysis for long-term planning: stratified decision-making models in sustainable supply chains - p. Page 107 18. Chapter six : Machine learning techniques for sustainable industrial process control - p. Page 136 19. Chapter seven : Intelligent sustainable infrastructure for procurement and distribution - p. Page 172 20. Chapter eight : Machine learning techniques for route optimizations and logistics management description - p. Page 194 21. Chapter nine : Revolutionizing sustainability: the role of robotics in supply chains - p. Page 223 22. Chapter ten : Computational techniques for sustainable green procurement and production - p. Page 268 23. Chapter eleven : Predictive big data analytics for supply chain demand forecasting - p. Page 296 24. Chapter twelve : Bayesian network based on cross bow-tie to analyze differential effects of internal and external risks on sustainable supply chain - p. Page 328 25. Chapter thirteen : Streamlining supply chain operations: A case study of bigbasket.com - p. Page 378 26. Chapter fourteen : Applications of artificial intelligence in Echo Global Logistics - p. Page 402 27. Index - p. Page 427
