期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 77, 期 -, 页码 186-196出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2018.10.008
关键词
Hyper-heuristic; Flexible job-shop scheduling; Fuzzy processing time; Solution decoding; Low-level heuristic
类别
资金
- National Natural Science Foundation of China [61503331, 61503330]
- Zhejiang Provincial Natural Science Foundation of China [Y19F030020]
- Zhejiang Key Laboratory of Solid State Drive and Data Security [2015E10003]
Flexible job-shop scheduling problem (FJSP) is among the most investigated scheduling problems over the past decades. The uncertainty of the processing time is an important practical characteristic in manufacturing. By considering the processing time to be fuzzy variable, the FJSP with fuzzy processing time (FJSPF) is more close to the reality. This paper proposes an effective backtracking search based hyper-heuristic (BS-HH) approach to address the FJSPF. Firstly, six simple and efficient heuristics are incorporated into the BS-HH to construct a set of low-level heuristics. Secondly, a backtracking search algorithm is introduced as the high-level strategy to manage the low-level heuristics to operate on the solution domain. Additionally, a novel hybrid solution decoding scheme is proposed to find an optimal solution more efficiently. Finally, the performance of the BS-HH is evaluated on two typical benchmark sets. The results show that the proposed hyper-heuristic outperforms the state-of-the-art algorithms in solving the FJSPF.
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