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Sinopsis
This book introduces and analyses recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches, including AI-based meta-heuristics applied to supply chain network models, integrated multi-criteria decision-making approaches for green supply chain management, uncertain supply chain models etc. 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.
Most of multi-national enterprises today face the challenge of sustainable development for their logistics systems trying to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. This book mainly encompasses the most popular and frequently employed AI-based meta-heuristics approaches such as genetic algorithm, variable neighborhood search, multi-objective heuristic search and the hybrid of these approaches.
The chapters in this book were originally published in the International Journal of Management Science and Engineering Management.
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Acerca del autor
Jiuping Xu is Associate Vice President of Sichuan University, P.R. China, and Editor-in-Chief of International Journal of Management Science and Engineering Management. His research interests include decision science, engineering management, and management science.
Mitsuo Gen is Senior Research Scientist of Fuzzy Logic Systems Institute and Visiting Professor at Tokyo University of Science, Japan. His research interests include soft computing, evolutionary algorithms, intelligent manufacturing, and sustainable closed supply chain.
Zongmin Li is Professor of Business School, Sichuan University, P. R. China, and Managing Editor of International Journal of Management Science and Engineering Management. Her research interests include data-driven decision making and big data analytics.
YoungSu Yun is Professor of Division of Business Administration at Chosun University, South Korea. His research interests include sustainable closed supply chain system, engineering optimization design and evolutionary algorithms.
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Taylor & Francis Ltd Dez 2023, 2023
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ISBN 13: 9781032634388
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces and analyses recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches, including AI-based meta-heuristics applied to supply chain network models, integrated multi-criteria decision-making approaches for green supply chain management, uncertain supply chain models etc. 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.Most of multi-national enterprises today face the challenge of sustainable development for their logistics systems trying to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. This book mainly encompasses the most popular and frequently employed AI-based meta-heuristics approaches such as genetic algorithm, variable neighborhood search, multi-objective heuristic search and the hybrid of these approaches.The chapters in this book were originally published in the International Journal of Management Science and Engineering Management. 174 pp. Englisch. Nº de ref. del artículo: 9781032634388
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Condición: New. Jiuping Xu is Associate Vice President of Sichuan University, P.R. China, and Editor-in-Chief of International Journal of Management Science and Engineering Management. His research interests include decision science, engineering management. Nº de ref. del artículo: 1027595052
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces and analyses recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches, including AI-based meta-heuristics applied to supply chain network models, integrated multi-criteria decision-making approaches for green supply chain management, uncertain supply chain models etc. 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.Most of multi-national enterprises today face the challenge of sustainable development for their logistics systems trying to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. This book mainly encompasses the most popular and frequently employed AI-based meta-heuristics approaches such as genetic algorithm, variable neighborhood search, multi-objective heuristic search and the hybrid of these approaches.The chapters in this book were originally published in the International Journal of Management Science and Engineering Management. Nº de ref. del artículo: 9781032634388
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