Multi-objective programming (MOP) can simultaneously optimize multi-objectives in mathematical programming models, but the optimization of multi-objectives triggers the issue of Pareto solutions and complicates the derived answers. To address these problems, researchers often incorporate the concepts of fuzzy sets and evolutionary algorithms into MOP models.
Focusing on the methodologies and applications of this field, Fuzzy Multiple Objective Decision Making presents mathematical tools for complex decision making. The first part of the book introduces the most popular methods used to calculate the solution of MOP in the field of multiple objective decision making (MODM). The authors describe multi-objective evolutionary algorithms; expand de novo programming to changeable spaces, such as decision and objective spaces; and cover network data envelopment analysis. The second part focuses on various applications, giving readers a practical, in-depth understanding of MODM.
A follow-up to the authors’ Multiple Attribute Decision Making: Methods and Applications, this book guides practitioners in using MODM methods to make effective decisions. It also extends students’ knowledge of the methods and provides researchers with the foundation to publish papers in operations research and management science journals.
Table of Contents
Introduction. CONCEPTS AND THEORY OF MULTI-OBJECTIVE DECISION MAKING: Multi-Objective Evolutionary Algorithms. Goal Programming. Compromise Solution and TOPSIS. De Novo Programming and Changeable Parameters. Multi-Stage Programming. Multi-Level Multi-Objective Programming. Data Envelopment Analysis. APPLICATIONS OF MULTI-OBJECTIVE DECISION MAKING: Motivation and Resource Allocation for Strategic Alliances through the De Novo Perspective. Choosing Best Alliance Partners and Allocating Optimal Alliance Resources Using the Fuzzy Multi-Objective Dummy Programming Model. Multiple-Objective Planning for Supply Chain Production and Distribution Model: Bicycle Manufacturer. Fuzzy Interdependent Multi-Objective Programming. Novel Algorithm for Uncertain Portfolio Selection. Multi-Objective Optimal Planning for Designing Relief Delivery Systems. Comparative Productivity Efficiency for Global Telecoms. Fuzzy Multiple Objective Programming in Interval Piecewise Regression Model. Bibliography. Notes.
Gwo-Hshiung Tzeng is a Distinguished Chair Professor at Kainan University. He is editor-in-chief of the International Journal of Operations Research and the International Journal of Information Systems for Logistics and Management. He received a PhD in management science from Osaka University. His research interests include statistics, multivariate analysis, networks, routing and scheduling, multiple criteria decision making, fuzzy theory, hierarchical structure analysis for application to technology management, energy, environment, transportation systems, transportation investment, logistics, location, urban planning, tourism, technology management, electronic commerce, and global supply chains.
Jih-Jeng Huang is an assistant professor of computer science and information management at Soochow University, where he teaches research methods, multivariate analysis, and capital asset and pricing models. He received a PhD in information management from the National Taiwan University. His research interests include multiple criteria decision making, knowledge management, behavioral economics and finance, and data analysis. His work has been widely published in journals and conference proceedings.