期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 52, 期 8, 页码 5295-5307出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2021.3120702
关键词
Job shop scheduling; Statistics; Sociology; Uncertainty; Indexes; Energy consumption; Genetic algorithms; Bi-population evolutionary algorithm; energy-efficient; feedback mechanism; flexible job shop scheduling problem; Fuzzy
资金
- National Science Fund for Distinguished Young Scholars of China [61525304]
- National Natural Science Foundation of China [61873328, 61573264]
This study focuses on energy-efficient fuzzy FJSP and proposes a bi-population evolutionary algorithm to optimize scheduling results. By handling uncertainty, dynamically adjusting population size, and using enhanced local search, the new method shows promising results in experiments.
The energy-efficient flexible job shop scheduling problem (FJSP) has attracted much attention in deterministic cases; however, uncertainty is seldom incorporated into energy-efficient FJSP and the neglecting of uncertainty will greatly diminish the application value of scheduling results. These make it necessary to handle uncertainty in the problem. In this study, energy-efficient fuzzy FJSP (EFFJSP) is considered and a bi-population evolutionary algorithm with feedback (FBEA) is proposed to minimize fuzzy makespan and fuzzy total energy consumption and maximize minimum agreement index. The computation of fuzzy energy consumption is given and four heuristics are proposed to produce the initial population. An effective method is presented to evaluate the quality of two populations and a feedback mechanism based on population quality is adopted to dynamically adjust the size of each population. A novel process of reproduction, crossover and mutation is developed based on feedback. An enhanced local search is also used to produce high-quality solutions. Extensive experiments are conducted to test the performance of FBEA. FBEA can provide promising results for EFFJSP.
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