Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

• Published in: International journal of production economics Vol. 141; no. 1; pp. 112 - 126
• Main Authors: Xiong, Jian, Xing, Li-ning, Chen, Ying-wu
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Breakdown; Breakdowns; Economics; Evolutionary algorithms; Expected values; Flexible job-shop schedule problem; Floats; Genetic algorithms; Job shops; Machine breakdowns; Multi-objective evolutionary algorithm; Operations research; Position (location); Production scheduling; Robust scheduling; Robustness; Scheduling; Studies; Surrogate measure; Flexible job-shop schedule problem; Robust scheduling; Machine breakdowns; Multi-objective evolutionary algorithm; Surrogate measure
• ISSN: 0925-5273; 1873-7579
• DOI: 10.1016/j.ijpe.2012.04.015

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives – makespan and robustness – are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases.