A Hybrid Genetic and Particle Swarm Optimization Algorithms for Dynamic Facility Layout Problem with Multiple Transporters

Nowadays, manufacturing plants should be agile to changes their production mix plan based on dynamic demands. Here, layout design significantly could impact on manufacturing efficiency. When the flows of materials between departments embed variability during the planning horizon, this problem is known as the dynamic facility layout problem (DFLP). This paper extends such problem with considering multiple transporters, which commonly are used for transportation tasks among facilities. Hence, we extended the classical DFLP objective function in such a way that could encounter total combined rearrangement, material handling and transporting costs. Firstly, the relevant mathematical model is presented and then hybrid metaheuristic algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) presented to solve such problem efficiently. To achieve reliable results, a Taguchi's design of experiments is applied to calibrate initial parameters. Also, a few small-sized problems are solved using the CPLEX software. Analysis of the results shows that the proposed hybrid PSO algorithms have good solution quality according to the objective function and CPU time rather than hybrid GA and proved the effectiveness of this algorithm on the set of test problems.