Due to the combinatorial nature of the facility layout problem (FLP), several heuristic and meta-heuristic approaches have been developed to obtain good rather than optimal solutions. Unfortunately, most of these approaches are predominantly on a single objective. However, the real-world FLPs are multi-objective by nature and only very recently have meta-heuristics been designed and used in multi-objective FLP. These most often use the weighted sum method to combine the different objectives and thus, inherit the well-known problems of this method. This paper presents an adaptive local search based genetic algorithm (GA) for solving the multi-objective FLP that presents the layouts as a set of Pareto-optimal solutions optimizing both quantitative and qualitative objectives simultaneously. Unlike the conventional local search, the proposed adaptive local search scheme automatically determines whether local search is used in a GA loop or not. The results obtained show that the proposed algorithm outperforms the other competing algorithms and can find near-optimal and non-dominated solutions by optimizing multiple criteria simultaneously. © 2010 Springer-Verlag.
CITATION STYLE
Ripon, K. S. N., Glette, K., Høvin, M., & Torresen, J. (2010). An adaptive local search based genetic algorithm for solving multi-objective facility layout problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6443 LNCS, pp. 540–550). https://doi.org/10.1007/978-3-642-17537-4_66
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