|Affiliations:||Mathematical Institute of the Serbian Academy of Sciences and Arts||Title:||An improved genetic algorithm for the multi level uncapacitated facility location problem||Journal:||International Journal of Computers, Communications and Control||Volume:||8||Issue:||6||First page:||845||Last page:||853||Issue Date:||1-Jan-2013||Rank:||M23||ISSN:||1841-9836||DOI:||10.15837/ijccc.2013.6.134||Abstract:||
In this paper, an improved genetic algorithm (GA) for solving the multilevel uncapacitated facility location problem (MLUFLP) is presented. First improvement is achieved by better implementation of dynamic programming, which speeds up the running time of the overall GA implementation. Second improvement is hybridization of the genetic algorithm with the fast local search procedure designed specially for MLUFLP. The experiments were carried out on instances proposed in the literature which are modified standard single level facility location problem instances. Improved genetic algorithm reaches all known optimal and the best solutions from literature, but in much shorter time. Hybridization with local search improves several best-known solutions for large-scale MLUFLP instances, in cases when the optimal is not known. Overall running time of both proposed GA methods is significantly shorter compared to previous GA approach.
|Keywords:||Combinatorial optimization | Discrete location | Evolutionary approach | Metaheuristics||Publisher:||Agora University|
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