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A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem
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DOI：10.1007/s10479-023-05541-w.png)
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Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem's NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS-BATCS(beta) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS-BATCS(beta) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average.
Keyword:
Serial batching
Incompatible job families
Sequence-dependent setup times
Arbitrary sizes
Total weighted tardiness
Heuristics
Machine learning
期刊
IF:
4.5
论文数:
8.1K
被引数:
2.1W
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