A NSGA-II Algorithm Hybridizing Local Simulated-Annealing Operators for a Bi-Criteria Robust Job-Shop Scheduling Problem Under Scenarios

This paper discusses a bi-criteria robust job-shop scheduling problem. Uncertain processing times are described by discrete scenario set. Two objectives are to minimize the mean makespan and to minimize the worst-case makespan among all scenarios, which reflect solution optimality and solution robus...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2019-05, Vol.27 (5), p.1075-1084
Main Authors: Wang, Bing, Xie, Hanxin, Xia, Xuedong, Zhang, Xianxia
Format: Article
Language:English
Subjects:
Algorithms
Bi-criteria problem
Computer simulation
Criteria
discrete scenarios
Genetic algorithms
Job shop scheduling
Job shops
non-dominated sorting genetic algorithm (NSGA-II)
Operators
Production scheduling
robust job-shop scheduling
Robustness
Robustness (mathematics)
Simulated annealing
simulated annealing (SA)
Sociology
Statistics
Uncertainty
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Summary:This paper discusses a bi-criteria robust job-shop scheduling problem. Uncertain processing times are described by discrete scenario set. Two objectives are to minimize the mean makespan and to minimize the worst-case makespan among all scenarios, which reflect solution optimality and solution robustness, respectively. The NSGA-II algorithm framework is incorporated with local simulated annealing (SA) operators to solve the proposed problem. The Metropolis criterion is defined by considering two objectives in order to evaluate bi-criteria individuals. United-scenario neighborhood structure is used in local SA operators to adapt to the uncertainty described by scenarios. The developed hybrid algorithm was compared with four alternative algorithms to obtain Pareto solution sets for the proposed bi-criteria problem in an extensive experiment. The computational results show that the developed algorithm obviously outperformed other algorithms in all test instances.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2018.2879789