Batching and resource allocation under stochastic unreliable flexible flow shop

Scheduling problems in manufacturing industries has received considerable attention from researchers under ideal environment in terms of certainty of information and stability of conditions. However, real life situations are much more complex, less stable and more uncertain. In such cases, researchers turn to the simulation methodology for optimizing scheduling decisions. In this study, a simulation model was built for the flexible flow shop problem using Arena and OptQuest was utilized to determine the best dispatching rule, optimal batch size and the number of machines at each work center. Average flow time and average total earliness and tardiness were selected as performance metrics. The studied problem is stochastic in terms of job arrival, processing time and machine failure. The problem becomes more complex with the existence of machine setup times in between job processing. Computational results are reported to show the effectiveness of the developed simulation model in finding desirable solutions for both objective functions. Different dispatching methods are tested and compared in terms of their effectiveness in optimizing different objective functions.