Multiple attribute decision making based on fuzzy preference information on alternatives: Ranking and weighting

Authors:
Ying-Ming Wang;Celik Parkan
Affiliations:
Manchester Business School, The University of Manchester, P.O. Box 88, Manchester M60 1QD, UK and School of Public Administration, Fuzhou University, Fuzhou 350002, PR China and Center for Account ...;Department of Management, C.W. Post Campus, Long Island University, 720 Northern Blvd., Brookville, NY 11548, USA
Venue:
Fuzzy Sets and Systems
Year:
2005

Citing 5
Cited 17

Group decision making with a fuzzy linguistic majority

Fuzzy Sets and Systems
On ordered weighted averaging aggregation operators in multicriteria decisionmaking

IEEE Transactions on Systems, Man and Cybernetics
Group decision making and consensus under fuzzy preferences and fuzzy majority

Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations

Fuzzy Sets and Systems
An approach to multiple attribute decision making based on fuzzy preference information on alternatives

Fuzzy Sets and Systems - Special issue: Soft decision analysis

Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment

Expert Systems with Applications: An International Journal
Fuzzy preference relations: Aggregation and weight determination

Computers and Industrial Engineering
Fusion and consistency of incomplete fuzzy preferences

CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Fuzzy Multi-criteria Decision Making Based on Fuzzy Preference Relation

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Extension of the expected value method for multiple attribute decision making with fuzzy data

Knowledge-Based Systems
Normalizing rank aggregation method for priority of a fuzzy preference relation and its effectiveness

International Journal of Approximate Reasoning
Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method

Expert Systems with Applications: An International Journal
A general multiple attribute decision-making approach for integrating subjective preferences and objective information

Fuzzy Sets and Systems
A multiple criteria decision method based on uncertain judgment

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
WEEE treatment strategies' evaluation using fuzzy LINMAP method

Expert Systems with Applications: An International Journal
Agent based e-commerce systems that react to buyers' feedbacks -- A fuzzy approach

International Journal of Approximate Reasoning
On group decision making with four formats of incomplete preference relations

Computers and Industrial Engineering
A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment

Information Sciences: an International Journal
Fuzzy multiple attributive group decision-making for conflict resolution in collaborative design

FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Multi-attribute group decision making models under interval type-2 fuzzy environment

Knowledge-Based Systems
Interval-valued hesitant preference relations and their applications to group decision making

Knowledge-Based Systems
Mutual information evaluation: A way to predict the performance of feature weighting on clustering

Intelligent Data Analysis

Quantified Score

Hi-index	0.21

Visualization

Abstract

This paper investigates the multiple attribute decision making (MADM) problem with fuzzy preference information on alternatives and proposes an eigenvector method to rank them. Three optimization models are introduced to assess the relative importance weights of attributes in a MADM problem, which integrate subjective fuzzy preference relations and objective information in different ways. Since the decisions these models lead to are based on both subjective and objective information, they are more balanced and credible than those that consider only one type of information. An example is provided to illustrate the application of the proposed models.