A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

Zigao Wu, Shaohua Yu and Tiancheng Li
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Zigao Wu: Department of Industrial Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Shaohua Yu: Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, 91190 Saint-Aubin, France
Tiancheng Li: Key Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

Mathematics, 2019, vol. 7, issue 6, 1-19

Abstract: In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.

Keywords: scheduling; evolutionary algorithm; robustness; multi-objective; machine breakdown (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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