Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 1, Pages 184-197Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2771213
Keywords
Genetic algorithm; lot-streaming; machine breakdown; rescheduling; robustness and stability criteria
Categories
Funding
- National Natural Science Foundation of China [61773384, 61673404, 61375067, 61374043, 61473299, 71533001, 61773192, 61503170, 61603169, 61773246, 61403155]
- Joint Research Fund for Overseas Chinese
- Hong Kong and Macau Scholars of National Natural Science Foundation of China [61428302]
- Shangdong Province Higher Educational Science and Technology Program [J14LN28, J17KZ005]
- Natural Science Foundation of Shandong Province [ZR2017BF039, ZR2016FL13]
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In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns. The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.
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