%I Elsevier Ltd
%A P. Vasant
%A N. Barsoum
%V 22
%T Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function
%P 767-777
%K Fuzzy logic; Fuzzy sets; Genetic algorithms; Nonlinear programming; Planning; Production control; Uncertainty analysis, Fuzzy-constraint; Hybrid evolutionary optimizations; Hybrid genetic algorithms; Hybrid optimization; Line searches; Logistic membership functions; Non-linear fitness functions; Uncertainty, Membership functions
%X Many engineering, science, information technology and management optimization problems can be considered as non-linear programming real-world problems where all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non-linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers, which was represented by logistic membership functions using the hybrid evolutionary optimization approach. To explore the applicability of the present study, a numerical example is considered to determine the production planning for the decision variables and profit of the company. © 2009 Elsevier Ltd. All rights reserved.
%L scholars804
%J Engineering Applications of Artificial Intelligence
%O cited By 78
%R 10.1016/j.engappai.2009.03.010
%N 4-5
%D 2009