A Genetic Algorithm to Find Pareto-optimal Solutions for the Dynamic Facility Layout Problem with Multiple Objectives

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.