Fuzzy Multiple Objective Decision Making
By Gwo-Hshiung Tzeng, Jih-Jeng Huang
To Be Published August 15th 2013 by Chapman and Hall/CRC – 322 pages
Description
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.
Contents
Introduction
Profile of Multiple Criterion Decision Making
Historical Development of Multiple Attribute Decision Making
Historical Development of Multiple Objective Decision Making
Introduction to Fuzzy Sets
Outline of the Book
Concepts and Theory of Multi-Objective Decision Making
Multi-Objective Evolutionary Algorithms
Concepts of Genetic Algorithms
GA Procedures
Multi-Objective Evolutionary Algorithms (MOEAs)
Goal Programming
Goal Setting
Weighted Goal Programming
Lexicography Goal Programming
Min–Max (Tchebycheff) Goal Programming
Fuzzy Goal Programming
Compromise Solution and TOPSIS
Compromise Solutions
TOPSIS for MODM
Fuzzy Compromise Solutions and TOPSIS
De Novo Programming and Changeable Parameters
De Novo Programming
De Novo Programming by Genetic Algorithms
De Novo Programming by Compromise Solution
Extensions of De Novo Programming
MOP with Changeable Parameters
Multi-Stage Programming
Dynamic Programming
Application of Multi-Stage Problem: Competence Sets
Fuzzy Multi-Stage Multi-Objective Competence Set
Multi-Level Multi-Objective Programming
Bi-Level Programming
Multiple Level Programming
Fuzzy Programming for Multi-Level Multi-Objective Programming
Data Envelopment Analysis
Traditional DEA
Network DEA
Fuzzy Multi-Objective Programming (FMOP) to DEA
Applications of Multi-Objective Decision Making
Motivation and Resource Allocation for Strategic Alliances through the De Novo Perspective
Motivations for Strategic Alliances
Problems of Resource Allocation
De Novo Perspective of Strategic Alliances
Numerical Example
Discussion
Conclusions
Choosing Best Alliance Partners and Allocating Optimal Alliance Resources Using Fuzzy Multi-Objective Dummy Programming Model
Review of Strategic Alliances
Fuzzy Multiple Objective Dummy Programming
Numerical Example
Discussion
Conclusions
Multiple-Objective Planning for Supply Chain Production and Distribution Model: Bicycle Manufacturer
Literature on Supply Chain and Multi-Objective
Programming for Production and Distribution
Establishing Model for Bicycle Supply Chain
Real Empirical Case of a Bicycle Manufacturer
Conclusions and Recommendations
Fuzzy Interdependent Multi-Objective Programming
Interdependence with Objectives
Fuzzy Interdependent Multi-Objective Programming
Numerical Example
Discussion
Conclusions
Novel Algorithm for Uncertain Portfolio Selection
Possibilistic Regression
Mellin Transformation
Numerical Example
Discussion
Conclusions
Multi-Objective Optimal Planning for Designing Relief Delivery Systems
Characteristics of Relief Distribution Systems
Relief Distribution Model
Relief Distribution Operation: Case Analysis
Case Illustration and Data Analysis
Conclusions and Recommendations
Comparative Productivity Efficiency for Global Telecoms
Global Telecommunication Trends
Data and Methods
Empirical Results and Discussions
Conclusions
Fuzzy Multiple Objective Programming in Interval Piecewise Regression Model
Introduction to Measure of Fitness and Fuzzy Multiple Objective Programming
Fuzzy Multiple Objective Programming in Piecewise Regression Model
Numerical Examples
Conclusions
Bibliography
Notes
Author Bio
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.
Name: Fuzzy Multiple Objective Decision Making (Hardback) – Chapman and Hall/CRC
Description: By Gwo-Hshiung Tzeng, Jih-Jeng Huang. 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...
Categories: Operations Research, Applied Mathematics, Systems & Control Engineering, Operations Research
