An uncertain permutation flow shop predictive scheduling problem with processing interruption

Jiayu Shen, Yuanji Shi, Jianxin Shi, Yunzhong Dai and Wei Li

Physica A: Statistical Mechanics and its Applications, 2023, vol. 611, issue C

Abstract: In this study, a permutation flow shop scheduling problem is examined. Due to a large number of uncertain factors in reality, the machine may be interrupted by many events during the processing. At this time, if the implementation is still carried out according to the original plan, it may deviate from the desired result. Therefore, the sudden machine failure is considered. The objective function is to find the pessimistic value of makespan. To explore the influence of uncertainty on decision variables and avoid frequent use of rescheduling strategy, a chance constrained programming model with faults is established. In accordance with the uncertainty theory, we derive the deterministic equivalence of the proposed model. A hybrid genetic algorithm combined with asynchronous evolution is proposed to solve this model. Additionally, the model is analyzed and special properties are proposed. Finally, the effectiveness of the modeling method is verified by numerical experiments. Moreover, it also shows that the hybrid genetic algorithm has greater advantages than the rescheduling strategy.

Keywords: Modeling; Flow shop; Uncertainty; Chance constrained; Genetic algorithm (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000122

DOI: 10.1016/j.physa.2023.128457

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