A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times
學年 114
學期 1
出版（發表）日期 2025-08-19
作品名稱 A Random Forest-Enhanced Genetic Algorithm for Order Acceptance Scheduling with Past-Sequence-Dependent Setup Times
作品名稱（其他語言）
著者 Yu-Yan Zhang; Shih-Hsin Chen; Yen-Wen Wang; Chia-Hsuan Liao; Chen-Hsiang Yu
單位
出版者
著錄名稱、卷期、頁數 Mathematics 13(16), 2672
摘要 This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely 𝑆𝐺𝐴𝑅𝐹 , 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 105 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), 𝑆𝐺𝐴𝑅𝐹 represents improvements of 0.9% over SGA, 0.8% over 𝑆𝐺𝐴𝐿𝑆 , 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.
關鍵字 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
語言 en
ISSN 2227-7390
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129683 )