By Topic

Fuzzy goal programming approach to chance constrained multiobjective decision making problems using genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Pal, B.B. ; Dept. of Math., Univ. of Kalyani, Kalyani, India ; Gupta, S. ; Biswas, P.

This paper presents how genetic algorithm (GA) can be used in fuzzy goal programming (FGP) formulation of multiobjective stochastic programming (SP) problems. In the proposed approach, the individual optimal decision of each of the objectives are determined by using the GA scheme adopted in the process of solving the problem after converting the chance constraints into their deterministic equivalent in. Then, the FGP model of the problem is formulated by introducing the concept of tolerance membership functions in fuzzy sets. In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. Two numerical examples are solved to illustrate the approach. The model solution of the first example is compared with the solution of the conventional approach studied previously.

Published in:

Industrial and Information Systems (ICIIS), 2009 International Conference on

Date of Conference:

28-31 Dec. 2009