Non-identical Parallel Machine Scheduling with Fuzzy Processing times Using Robust Genetic Algorithm and Simulation

This paper addresses non-identical parallel machine scheduling problem with fuzzy processing times (FPMSP). A robust genetic algorithm (GA) approach embedded in a simulation model to minimize maximum completion time (makespan) is proposed. The results are compared with those obtained by using LPT rule, known as the most appropriate dispatching rule for such problems. This application illustrates the need for efficient and effective heuristics to solve FPMSPs. The proposed GA approach yields good results and reaches them fast and several times in one run. Moreover, due to its advantage of being 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 it has been shown that the proposed approach is robust.