An Agent Gaming and Genetic Algorithm Hybrid Method for Factory Location Setting and Factory/Supplier Selection Problems
This paper first presents two supply chain design problems: 1) a factory location setting and factory selection problem, and 2) a factory location setting and factory/supplier selection problem. The first involves a number of location known retailers choosing one factory to supply their demands from a number of factories whose locations are to be determined. The goal is to minimize the transportation and manufacturing cost to satisfy the demands. The problem is then augmented into the second problem, where the procurement cost of the raw materials from a chosen material supplier (from a number of suppliers) is considered for each factory. Economic beneficial is taken into account in the cost evaluation. Therefore, the partner selections will influence the cost of the supply chain significantly. To solve these problems, an agent gaming and genetic algorithm hybrid method (AGGAHM) is proposed. The AGGAHM consecutively and alternatively enable and disable the advancement of agent gaming and the evolution of genetic computation. Computation results on solving a number of examples by the AGGAHM were compared with those from methods of a general genetic algorithm and a mutual frozen genetic algorithm. Results showed that the AGGAHM outperforms the methods solely using genetic algorithms. In addition, various parameter settings are tested and discussed to facilitate the supply chain designs.
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