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Fuzzy Multiple Objective Decision Making

By Gwo-Hshiung Tzeng, Jih-Jeng Huang

Chapman and Hall/CRC – 2013 – 322 pages

Purchasing Options:

  • Add to CartHardback: $129.95
    978-1-46-655461-0
    August 8th 2013

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