An Improved Genetic Algorithm for the Multi Level Uncapacitated Facility Location Problem
Keywords:evolutionary approach, metaheuristics, discrete location, combinatorial op- timization.
AbstractIn this paper, an improved genetic algorithm (GA) for solving the multi-level uncapacitated facility location problem (MLUFLP) is presented. First improvement is achieved byÂ better implementation of dynamic programming, which speeds up the running time of theÂ overall GA implementation. Second improvement is hybridization of the genetic algorithmÂ with the fast local search procedure designed specially for MLUFLP. The experiments wereÂ carried out on instances proposed in the literature which are modied standard single levelÂ facility location problem instances. Improved genetic algorithm reaches all known optimalÂ and the best solutions from literature, but in much shorter time. Hybridization with localÂ search improves several best-known solutions for large-scale MLUFLP instances, in casesÂ when the optimal is not known. Overall running time of both proposed GA methods isÂ signicantly shorter compared to previous GA approach.
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