期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 11, 页码 12112-12125出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3081805
关键词
Heuristic algorithms; Job shop scheduling; Sequential analysis; Iron; Buffer storage; Approximation algorithms; Steel; Artificial immune; continuous limited output buffer; critical path; job shop scheduling; multiroute; simulated annealing (SA)
类别
资金
- Major Research Plan of the National Major Research Program [2018AAA0101604]
- Key Project of the National Natural Science Foundation of China [61936009]
- National Natural Science Foundation of China [2018YFB1702903]
- National Key Research and Development Program of China [2017YFB0304102]
- DongGuan Innovative Research Team Program [2018607202007]
In this article, the multiroute job shop scheduling problem with continuous-limited output buffers (MRJSP-CLOBs) is studied, and a hybrid algorithm AIA-SA is proposed, which shows lower computing time and faster and more accurate performance in large-scale instances compared to other algorithms.
In this article, we study the multiroute job shop scheduling problem with continuous-limited output buffers (MRJSP-CLOBs). In contrast to the standard job shop scheduling problem (JSP), continuous-limited output buffers render the commonly used graph-based approaches inapplicable, and the multiroute issue further increases computational complexity. To this end, we formulate MRJSP-CLOB as a mixed-integer linear program (MILP), which is typically NP-hard. Then, we extend the critical block in the JSP by utilizing the no-time-gap relationship and design a new neighborhood structure. Furthermore, we propose a hybrid artificial immune-simulated annealing algorithm (AIA-SA) by sharing iterations and integrating a random infeasible solution repairing algorithm with a new SA acceptance rule, which enables individuals to share information and increases the robustness of the corresponding SA parameters. Finally, the AIA-SA is compared with CPLEX and state-of-the-art algorithms on MRJSP-CLOB with different sizes. Experiments for large-sized instances demonstrate that our algorithm requires less than 3% computing time of the CPLEX, while being faster and more accurate than the other algorithms.
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