This book explores the intersection of data envelopment analysis (DEA) and various analytical decision-making methodologies. Featuring contributions from experts in the field from across the world, each chapter delves into different aspects of DEA and its applications in real-world scenarios. The book covers a wide range of topics, including integrating DEA with machine learning techniques, performance evaluation in diverse sectors like banking and civil engineering, and using DEA in managerial decision-making. It also examines data mining during the Covid-19 pandemic and the application of blockchain and IoT in supply chain management. The book offers a deep dive into the evolution of nonparametric frontier methods and the development of new optimization algorithms, addressing the complexities of modern analytical decision-making tools.A few chapters delve into futuristic topics like fuzzy sets and their extensions in decision-making and exploring e-learning platforms for education. This book is an invaluable resource for researchers, practitioners and students interested in the latest DEA advancements and practical applications in various fields. Its multidisciplinary approach makes it a useful addition to the libraries of those seeking to understand the complexities and potentials of modern analytical decision-making tools.
Auteur
Seyyed Ahmad Edalatpanah is Associate Professor at the Department of Applied Mathematics and the Chief of Research and Development at the Ayandegan Institute of Higher Education, Tonekabon, Iran. He received his PhD in applied mathematics from the University of Guilan, Rasht, Iran. His fields of interest are data envelopment analysis, uncertainty, fuzzy mathematics, numerical linear algebra, soft computing and optimization. With more than 100 papers in journals and conference proceedings, he serves on the editorial boards of several international journals. He is also the Editor-in-Chief of the International Journal of Research in Industrial Engineering.
Farhad Hosseinzadeh Lotfi is Full Professor of Mathematics at the Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran. He received his PhD in Applied Mathematics from the Science and Research Branch at IAU. His primary research interests are operations research and data envelopment analysis. He has published more than 300 scientific and technical papers in leading scientific journals. He is Editor-in-Chief of the Data Envelopment Analysis and Decision Science journal. He is also Director-in-Charge and on the editorial board of the International Journal of Industrial Mathematics.
Kristiaan Kerstens is Research Professor at CNRS-LEM and Full Professor of Economics at the IESEG School of Management in Lille, France. His primary research focuses on developing a nonparametric methodology to analyse microeconomic production and portfolio behaviour. His work covers productivity indices and indicators, efficiency measurement (with a particular interest in nonconvexities), capacity utilization and multi-moment portfolio optimization. His publications appeared in outlets like the American Journal of Agricultural Economics, Economic Theory, European Journal of Operational Research, International Journal of Production Economics, Journal of Banking and Finance, Journal of Economic Dynamics and Control, Management Science, Scandinavian Journal of Economics, and Operations Research.
Peter Wanke is Visiting Scholar at the Department of Marketing and Logistics of Ohio State University, USA. He is a Doctor of Sciences in Product Engineering from Graduation Programs in Engineering for Federal University of Rio De Janeiro (COPPE/UFRJ). He works in teaching, research and consultancy in the fields of facilities location, logistics and transport systems simulation, demand planning forecast, inventory management in supply chains, business unit efficiency analysis, and logistics strategy. With more than 60 papers published in conferences, magazines and journals, he is co-editor of many books and the author of Inventory Management in the Supply Chain: Decisions and Quantitative Models.