FIREFLY ALGORITHM HYBRIDIZED WITH GENETIC ALGORITHM FOR MULTI-OBJECTIVE INTEGRATED PROCESS PLANNING AND SCHEDULING

The multi-objective integrated process planning and scheduling (MOIPPS) problem has a huge search space and complex technical constraints. Therefore, there is considerable difficulty in obtaining efficient solutions, and hence, metaheuristic-based solution algorithms have been actively introduced. In our paper, we propose a method to obtain a set of Pareto solutions using a firefly algorithm hybridized with a genetic algorithm for the MOIPPS problem. We considered a MOIPPS problem model that simultaneously optimizes the makespan, total flow time and total tardiness, maximum machine workload and total machine workload. Several different scale instances have been employed to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm has excellent performance in solving the MOIPPS problem.