This book emphasizes both theory and practice, providing methodological and theoretical basis as case references for Sustainable Logistics Systems using AI based Meta Heuristics. It encompasses the most frequently employed AI-based meta-heuristics approaches.

Introduction - Sustainable Logistics Systems using AI-based Meta-Heuristics Approaches 1. Applying GA-VNS approach to supply chain network model with facility and route disruptions 2. Edge boundary variable neighborhood strategy adaptive search for a vegetable crop land allocation problem 3. Multi-criteria decision-making methods for the evaluating of a real green supply chain in companies with fast-moving consumer goods 4. Multi-objective grouping genetic algorithm for the joint order batching, batch assignment, and sequencing problem 5. Green supply chain management framework for supplier selection: an integrated multi-criteria decision-making approach 6. A dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study 7. Robust and resilient supply chain network design considering risks in food industry: flavour industry in Iran 8. Modeling resilient supplier selection criteria in desalination supply chain based on fuzzy DEMATEL and ISM 9. Designing a data-driven leagile sustainable closed-loop supply chain network 10. Designing an integrated decision support system to link supply chain processes performance with time to market 11. Application of expected value and chance constraint on uncertain supply chain model with cost, risk and visibility for COVID-19 pandemic 12. Optimal storage and shipment management of perishable agri-products with a hybrid priority policy: a case study