Robust Metaheuristics for Scheduling Cellular Flowshop with Family Sequence-Dependent Setup Times

In manufacturing systems, minimization of the total flow time has a great impact on the production time, the productivity and the profitability of a firm. This paper considers a cellular flowshop scheduling problem with family sequence setup time to minimize the total flow time. Two metaheuristic algorithms based on Genetic algorithm (GA) and particle swarm optimization (PSO) are proposed to solve the proposed problem. As it is customarily accepted, the performance of the proposed algorithms is evaluated using Design of Experiments (DOE) to study the robustness of the proposed metaheuristics based on the Relative Percentage Deviation (RPD) from the lower bounds. The results of the DOE evaluation of the proposed algorithms show that PSO-based metaheuristic is better than GA for solving scheduling problems in cellular flow shop, which aims to minimize the total flow time.