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Genetic algorithm approach for large scale quadratic programming of probabilistic supplier selection and inventory management problem

Last modified: 2019-05-29

#### Abstract

This paper is considered to observe how an existing metaheuristic optimization method, genetic algorithm is suitable or not to be used to solve a large-scale integer quadratic programming of a probabilistic supplier selection with inventory management problem. Word “probabilistic” in this case is refer to that the problem is involving some uncertain parameters approached by random variable (probabilistic parameter). We used the existing mathematical model of probabilistic supplier selection problem with inventory management provided in our previous works that only considering few numbers of decision variable then the occurred optimization problem is a small-scale problem that can be solved efficiently by analytical method or numerical method. Then, in this paper we resolved this model with huge number of decision variable indicated by the number of the supplier and time period that is large by using an existing metaheuristics method, genetic algorithm to analyse how the decision variable, is it reliable to be used or not. We generate some randomly data to simulate the problem and the results. we have run hundreds of computational experiments. From the results, the decision obtained by the genetic algorithm was significantly differ from the global optimal solution generated by generalized reduced gradient (GRD) performed in LINGO 18.0. In conclusion, GA is not preferred to solve the large-scale problem of supplier selection and inventory management.