期刊
JOURNAL OF HEURISTICS
卷 27, 期 1-2, 页码 175-204出版社
SPRINGER
DOI: 10.1007/s10732-020-09458-6
关键词
Just-in-time scheduling; Earliness and tardiness; Matheuristic; Heuristic; Variable neighborhood search; Relax-and-solve
资金
- UTS International Research Scholarship (IRS)
- UTS Faculty of Science Scholarship
- Australian Research Council - Australian Government [DE170100234]
- Australian Research Council [DE170100234] Funding Source: Australian Research Council
The JIT-JSS problem involves operations with distinct due-dates and penalties for early or late completion. The proposed matheuristic algorithm outperforms existing methods by decomposing and optimizing sub-problems, delivering optimal schedules for a majority of instances.
In the just-in-time job-shop scheduling (JIT-JSS) problem every operation has a distinct due-date, and earliness and tardiness penalties. Any deviation from the due-date incurs penalties. The objective of JIT-JSS is to obtain a schedule, i.e., the completion time for performing the operations, with the smallest total (weighted) earliness and tardiness penalties. This paper presents a matheuristic algorithm for the JIT-JSS problem, which operates by decomposing the problem into smaller sub-problems, optimizing the sub-problems and delivering the optimal schedule for the problem. By solving a set of 72 benchmark instances ranging from 10 to 20 jobs and 20 to 200 operations we show that the proposed algorithm outperforms the state-of-the-art methods and the solver CPLEX, and obtains new best solutions for nearly 56% of the instances, including for 79% of the large instances with 20 jobs.
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