Sustainable Logistics Systems Using AI-based Meta-Heuristics Approaches

This book discusses recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches, emphasizing both theory and practice with methodological and theoretical basis and case references. It covers popular AI-based meta-heuristics like genetic algorithm, variable neighborhood search, multi-objective heuristic search, and their hybrid applications in resolving complex logistics and supply chain problems.

Format: Hardback
Length: 174 pages
Publication date: 22 December 2023
Publisher: Taylor & Francis Ltd

Sustainable logistics systems have gained significant attention in recent years, driven by the need to meet customer expectations while minimizing environmental impact. In this context, artificial intelligence (AI) based meta-heuristics approaches have emerged as powerful tools for optimizing complex logistics and supply chain problems. This book aims to introduce and analyze the latest trends and studies in sustainable logistics systems using AI-based meta-heuristics.

The book begins by providing an overview of sustainable logistics systems and their importance in today's business landscape. It then delves into the various AI-based meta-heuristics approaches used in these systems, including genetic algorithm, variable neighborhood search, multi-objective heuristic search, and their hybridization.

Chapter 1 discusses the theoretical foundations of AI-based meta-heuristics and their application in sustainable logistics systems. It explores the principles of optimization, search algorithms, and their suitability for solving complex logistics problems. The chapter also highlights the benefits of using AI-based meta-heuristics, such as their ability to handle large datasets, handle uncertainty, and provide efficient solutions.

Chapter 2 explores the use of AI-based meta-heuristics in supply chain network models. It discusses the development of optimization models for supply chain networks, including demand forecasting, inventory management, and routing. The chapter also highlights the challenges associated with supply chain network modeling and the role of AI-based meta-heuristics in addressing them.

Chapter 3 discusses the integration of multi-criteria decision-making approaches for green supply chain management. It explores the use of AI-based meta-heuristics in evaluating and selecting sustainable supply chain options, such as renewable energy sources, eco-friendly packaging, and waste reduction strategies. The chapter also discusses the challenges associated with integrating multiple criteria and the role of AI-based meta-heuristics in overcoming them.

Chapter 4 discusses the use of AI-based meta-heuristics in uncertain supply chain models. It explores the development of optimization models for supply chains facing uncertainty, such as supply disruptions, demand fluctuations, and natural disasters. The chapter also highlights the challenges associated with uncertain supply chains and the role of AI-based meta-heuristics in addressing them.

Chapter 5 discusses the case studies and case applications of sustainable logistics systems using AI-based meta-heuristics. It showcases real-world examples of companies that have successfully implemented AI-based meta-heuristics in their logistics systems, including Amazon, FedEx, and Walmart. The chapter also discusses the benefits and challenges associated with these case studies and provides insights into the future of sustainable logistics systems using AI-based meta-heuristics.

In conclusion, this book provides a comprehensive and up-to-date overview of sustainable logistics systems using AI-based meta-heuristics approaches. It emphasizes both theory and practice, providing methodological and theoretical basis as well as case references for sustainable logistics systems using AI based meta-heuristics. The chapters in this book were originally published in the International Journal of Management Science and Engineering Management.

Weight: 500g
Dimension: 297 x 210 (mm)
ISBN-13: 9781032634388