Management Science
Genetic search and the dynamic facility layout problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Using pareto-optimality for solving multi-objective unequal area facility layout problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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In today's volatile manufacturing scenario, manufacturing facilities must operate in a dynamic and market-driven environment in which production rates and production mixes are continuously changing. To operate efficiently within such an environment, the facility layout needs to be adaptable to changes. The dynamic facility layout problem (DFLP) deals with changes of layout over time. DFLPs are usually solved just considering quantitative aspect of layout alone, ignoring the qualitative aspect. Few attempts have been made to date to deal with the multi-objective DFLP. These most often use the weighted-sum method to combine different objectives and thus, inherit the wellknown problems of this method. The objective of this paper is to introduce an evolutionary approach for solving multi-objective DFLP that presents the layout as a set of Pareto-optimal solutions optimizing both quantitative and qualitative objectives simultaneously. Experimental results obtained with the proposed approach are promising.