A Hybrid Genetic Algorithm Approach for Optimizing Dynamic Multi-Commodity Supply Chains
نویسندگان
چکیده
In this paper, a dynamic supply chain network (SCN) model is developed in which both dynamic facility locations and dynamic distributed quantities of materials and products are assumed. The problem is formulated mathematically using the mixed integer linear programming (MILP). The solution methodology adopted in this research is the hybrid genetic algorithm (hGA) comprising genetic algorithm (GA) and pattern search (PS) optimization techniques. A study is conducted in this research and experimental work is performed in order to validate the proposed hGA. In this study, several comparisons are made between the solutions of the designed hGA and the solutions of the branch-and-bound algorithm used by the LINGO 12 as a commercial package. Results show that the developed hGA is an appropriate and a useful solution methodology for largesize SCN with a small number of manufactured products and used raw materials.
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