Zongmin Li – författare

Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making. The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection. The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.

Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making. The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection. The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.

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.

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.

This book offers a comprehensive exploration of the latest advancements in sustainable and resilient supply chain management. The chapters in this volume present a diverse range of topics, including innovative strategies for optimizing sustainable food supply chains through data envelopment analysis, dynamic multi-modal perspectives on global supply chain network configuration, and robust data-driven optimization techniques for designing sustainable cold supply chains for livestock and perishable goods. Other chapters delve into key areas such as optimal pricing and production lot size strategies for sustainable inventory models, carbon-sensitive transport solutions for deteriorating supply chains, and trade credit policies in growing product supply chains.Additionally, the book addresses the complexities of inventory management, purchasing, and cutting policies for imperfect and perishable raw materials, the influence of supply chain finance and trade digitization on financial stability, and the development of heuristic master planning algorithms that prioritize fairness and flexibility. Further studies examine adaptive product changeover policies, a PDCA-based approach to evaluating green supply chain performance, and robust multi-criteria decision analysis techniques for selecting transportation service providers in the face of uncertainty. By offering insights into both theoretical and practical aspects, this book provides a valuable resource for researchers, practitioners, and policymakers looking to navigate the evolving landscape of sustainable supply chains. This book will be valuable to students, researchers and policymakers in the fields of supply chain management, sustainable logistics and operations research.The chapters in this book were originally published in International Journal of Management Science and Engineering Management.

This book presents a cohesive collection of cutting-edge research focused on advanced decision-support methodologies for complex and ambiguous operational contexts. This volume systematically assembles ten pivotal studies employing uncertain decision-making, multi-criteria decision analysis (MCDA), and tailored optimization models to address substantive challenges in supply chain management, sustainable operations, and strategic planning.The contributions span a rigorous methodological spectrum, including fuzzy PDCA cycles for green performance evaluation, hybrid MCDM frameworks for lean tool selection, and robust optimization for resilient network design. Applications are drawn from critical domains such as healthcare logistics during epidemic response, sustainable supply chain configuration, and large-scale project trade-off analysis under resource constraints. Each chapter demonstrates the translation of theoretical computational models into applied solutions for decision-making under volatility, incomplete information, and multi-objective pressures.This volume will serve as a significant reference for academics and graduate researchers in management science, operations research, and industrial engineering. It provides a structured examination of how contemporary soft computing and operational research techniques can be leveraged to enhance analytical rigor and strategic outcomes in the face of pervasive environmental uncertainty.The chapters in this book were published in International Journal of Management Science and Engineering Management.