A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times

Yu-Yan Zhang, Shih-Hsin Chen (), Yen-Wen Wang, Chia-Hsuan Liao and Chen-Hsiang Yu
Additional contact information
Yu-Yan Zhang: Department of Information Management, Cheng Shiu University, Kaohsiung City 833, Taiwan
Shih-Hsin Chen: Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 251, Taiwan
Yen-Wen Wang: Department of Industrial Engineering and Management, Minghsin University of Science and Technology, Hsinchu County 304, Taiwan
Chia-Hsuan Liao: Graduate Institute of Educational Psychology and Counseling, Tamkang University, New Taipei City 251, Taiwan
Chen-Hsiang Yu: Multidisciplinary Graduate Engineering, College of Engineering, Northeastern University, Boston, MA 02115, USA

Mathematics, 2025, vol. 13, issue 16, 1-24

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.

Keywords: permutation flow-shop scheduling (PFSS) with order acceptance; order acceptance and scheduling (OAS) problem; past-sequence-dependent (PSD); genetic algorithm; random forest (RF); local search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/16/2672/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/16/2672/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:16:p:2672-:d:1728010

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().