Paper Info
Title
A New Possibilistic Programming Approach For Solving Fuzzy Multiobjective Assignment Problem
Abstract
In this paper, we propose a new possibilistic programming approach to solve a fuzzy multiobjective assignment problem in which the objective function coefficients are characterized by triangular possibility distributions. The proposed solution approach simultaneously minimizes the best scenario, the likeliest scenario, and the worst scenario for the imprecise objective functions using $\\alpha$ -level sets. The $\\alpha$-level sets are used to define the confidence level of the fuzzy judgments of the decision maker. Additionally, we provide a systematic framework in which the decision maker controls the search direction by updating both the membership values and aspiration levels until a set of satisfactory solutions is obtained. Numerical examples, with dataset from realistic situations, are provided to demonstrate the effectiveness of the proposed approach.
Year
DOI
Venue
2014
10.1109/TFUZZ.2013.2245134
Fuzzy Systems, IEEE Transactions  
Keywords
Field
DocType
decision making,fuzzy set theory,possibility theory,programming,aspiration levels,decision maker,fuzzy multiobjective assignment problem,imprecise objective functions,membership values,objective function coefficients,possibilistic programming,search direction,systematic framework,triangular possibility distributions,worst scenario,$alpha$-level sets,assignment problem,fuzzy mathematical programming,possibility theory,triangular fuzzy numbers
Mathematical optimization,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Possibility theory,Fuzzy set,Assignment problem,Artificial intelligence,Type-2 fuzzy sets and systems,Membership function,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
22
1
1063-6706
Citations 
PageRank 
References 
5
0.46
12
Authors
2
Name
Order
Citations
PageRank
Pankaj Gupta11479133.85
Mukesh Kumar Mehlawat227522.90