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A Variable Neighborhood Search Algorithm for Solving Fuzzy Number Linear Programming Problems Using Modified Kerre's Method
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DOI：10.1109/TFUZZ.2018.2876690.png)
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Abstract
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To solve a fuzzy linear program, we need to compare fuzzy numbers. Here, we make use of our recently proposed modified Kerre's method for comparison of LR fuzzy numbers. We give some new results on LR fuzzy numbers and show that to compare two LR fuzzy numbers, it is not necessary to compute the fuzzy maximum of two numbers directly. Using the modified Kerre's method, we propose a new variable neighborhood search algorithm for solving fuzzy number linear programming problems. In our algorithm, the local search is defined based on descent directions, which are found by solving four crisp mathematical programming problems. In several methods, a fuzzy optimization problem is converted to a crisp problem but in our proposed method, using our modified Kerre's method, the fuzzy optimization problem is solved directly, without changing it to a crisp program. We provide examples to compare the performance of our proposed algorithm to other available methods. We show the effectiveness of our proposed algorithm by using the nonparametric statistical sign test.
Keywords:
Fuzzy linear programming problem
modified Kerre's method
ranking function
variable neighborhood search (VNS) algorithm
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