A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times.
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| Title: | A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times. |
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| Authors: | Zhang, Yu-Yan, Chen, Shih-Hsin, Wang, Yen-Wen, Liao, Chia-Hsuan, Yu, Chen-Hsiang |
| Source: | Mathematics (2227-7390); Aug2025, Vol. 13 Issue 16, p2672, 23p |
| Subject Terms: | FLOW shop scheduling, GENETIC algorithms, SCHEDULING, SETUP time, RANDOM forest algorithms, FORECASTING, EVOLUTIONARY algorithms, RESOURCE allocation |
| Abstract: | This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely S G A R F , to solve the permutation flow-shop scheduling problem with order acceptance under the conditions of limited capacity, weighted-tardiness, and past-sequence-dependent (PSD) setup times (PFSS-OAWT with PSD). To the best of our knowledge, this is the first study to investigate this problem. Our proposed algorithm increases the setup time for each successive job by a constant proportion of the cumulative processing time of preceding jobs to capture the progressive slowdown that often occurs on real production lines. In the developed algorithm with maximum 10 5 fitness evaluations, the RF surrogate model predicts objective function values and guides crossover and mutation. On the PFSS-OAWT with PSD benchmark (up to 500 orders and 20 machines, 160 instances), S G A R F represents improvements of 0.9% over SGA, 0.8% over S G A L S , and 5.6% over SABPO. Although the surrogate incurs additional runtime, the gains in both profit and order-acceptance rates justify its use for high-margin, offline planning. Overall, the results of this study suggest that integrating evolutionary search into data-driven prediction is an effective strategy for solving complex capacity-constrained scheduling problems. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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