Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation

This paper addresses parallel machine scheduling problems with fuzzy processing times. A robust genetic algorithm (GA) approach embedded in a simulation model is proposed to minimize the maximum completion time (makespan). The results are compared with those obtained by using the “longest processing time” rule (LPT), which is known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve such fuzzy parallel machine scheduling problems (FPMSPs). The proposed GA approach yields good results quickly and several times in one run. Moreover, because it is a search algorithm, it can explore alternative schedules providing the same results. Thanks to the simulation model, several robustness tests are conducted using different random number sets, and the robustness of the proposed approach is demonstrated.