Heterogeneous Group Decision Making Models Under Linguistic Assessment: TOPSIS, Ordinal Approach, Aggregation Under Fuzzy Environment - Softcover

Anisseh, Mohammad; Shahraki, Mohammad Reza

Softcover

ISBN 10: 3848431351 ISBN 13: 9783848431359

Publisher: LAP LAMBERT Academic Publishing, 2012

Synopsis

Multi criteria group decision making (MCGDM) methods are broadly used in the real-world decision circumstances for homogeneous groups. In this book, heterogeneous group decision making models under fuzzy environment for multi-dimensional personnel evaluation were proposed to compensate the differences of decision makers’ knowledge such as: education, expertise, experience and other aspects. A new fuzzy group decision making method was developed under the linguistic framework for heterogeneous group decision making that aims at a desired consensus. Besides, the classical ordinal approach method under a linguistic framework is developed for heterogeneous group decision making, which allows group members to express their fuzzy preferences in linguistic terms for alternative selection and for individual judgments. Furthermore, a fuzzy extension of technique for order preference by similarity to ideal solution (TOPSIS) method under fuzzy environment was proposed. In order to solve the problem of discrepancy between decision making methods’ results, a new optimization method was developed, to aggregate the results’ of different decision making models.

About this title

About the Author

M. Anisseh, Assistant Professor in the Department of Industrial Management, Imam Khomeini International University, Iran.Mohammad Reza Shahraki, Assistant Professor in the Departmentof Industrial Engineering, University of Sistan and Baluchestan, Iran.They received their PhD in Industrial and Systems Engineering from University Putra Malaysia.

Edition details

• Publisher: LAP LAMBERT Academic Publishing
• Publication date: 2012
• Language: English
• ISBN 10: 3848431351
• ISBN 13: 9783848431359
• Binding: Paperback
• Number of pages: 228