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When we consider the real-time situation in scheduling problems, it always helps to enhance the manufacturing system and increases the system performance. In this study, the effect of five input parameters, i.e., reliability-centered preventive maintenance, percentage of machine failure (PMF), mean time to repair for random machine breakdown, due date tightness factor, and routing flexibility (RF) on stochastic flexible job shop scheduling problem (SFJSSP) under simultaneously reliability-centered preventive maintenance and random machine breakdown environment with sequence-dependent setup time is evaluated. The effects of input parameters are measured using four different performance measures, i.e., mean flow time (MFT), makespan (C max ), mean tardiness (MT), and total setups (TS). A statistical response surface methodology is used to assess the performance measures. ANOVA analysis is used to determine the model’s suitability. The results show that PMF and RF are found as the most common significant input factors for all the performance measures. Multi-objective optimization is performed using the desirability function approach to optimize the system performance measures. It is found that the minimum value of MFT, C max , MT, and TS performance measures for optimum performance of the SFJSSP are predicted as 123.432, 220,561, 103.399, and 102,171, respectively, with composite desirability, D of 0.916. The confirmatory results show that the error between the predicted and experimental results is less than 5%. Moreover, considering both uncertainties with dynamic jobs arrival environment shows the study’s real-time scheduling scenario and novelty.

Analysis of Integrated Preventive Maintenance and Machine Failure in Stochastic Flexible Job Shop Scheduling with Sequence-dependent Setup Time