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International Journal of Production Research Volume 60, 2022 - Issue 13: Special Issue: Analysis, Design, and Optimization in Smart and Connected Production and Service Systems
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Research Article

A novel feature selection for evolving compact dispatching rules using genetic programming for dynamic job shop scheduling

Salama ShadyGraduate School of System Informatics, Kobe University, Kobe, JapanCorrespondence[email protected]
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,
Toshiya KaiharaGraduate School of System Informatics, Kobe University, Kobe, JapanView further author information
,
Nobutada FujiiGraduate School of System Informatics, Kobe University, Kobe, JapanView further author information
&
Daisuke KokuryoGraduate School of System Informatics, Kobe University, Kobe, JapanView further author information
Pages 4025-4048 | Received 30 May 2021, Accepted 06 Mar 2022, Published online: 29 Mar 2022

Citations (17)

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Amir Ahmadi-Javid, Maryam Haghi & Pedram Hooshangi-Tabrizi. (2024) Integrated job-shop scheduling in an FMS with heterogeneous transporters: MILP formulation, constraint programming, and branch-and-bound. International Journal of Production Research 62:9, pages 3288-3304.
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Yannik Zeiträg, José Rui Figueira & Gonçalo Figueira. (2024) A cooperative coevolutionary hyper-heuristic approach to solve lot-sizing and job shop scheduling problems using genetic programming. International Journal of Production Research 0:0, pages 1-28.
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Articles from other publishers (15)

Lubo Li, Haohua Zhang & Sijun Bai. (2024) A multi-surrogate genetic programming hyper-heuristic algorithm for the manufacturing project scheduling problem with setup times under dynamic and interference environments. Expert Systems with Applications 250, pages 123854.
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Xinquan Wu, Xuefeng Yan, Donghai Guan & Mingqiang Wei. (2024) A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time. Engineering Applications of Artificial Intelligence 131, pages 107790.
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Zhenyu Liu, Haoyang Mao, Guodong Sa, Hui Liu & Jianrong Tan. (2024) Dynamic job-shop scheduling using graph reinforcement learning with auxiliary strategy. Journal of Manufacturing Systems 73, pages 1-18.
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Fangfang Zhang, Yi Mei, Su Nguyen & Mengjie Zhang. (2024) Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling. IEEE Transactions on Evolutionary Computation 28:1, pages 147-167.
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Haojie Chen, Xinyu Li & Liang Gao. (2023) A guided genetic programming with attribute node activation encoding for resource constrained project scheduling problem. Swarm and Evolutionary Computation 83, pages 101418.
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Xinquan Wu & Xuefeng Yan. (2023) A spatial pyramid pooling-based deep reinforcement learning model for dynamic job-shop scheduling problem. Computers & Operations Research 160, pages 106401.
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Baolin Fei, Binzi Xu, Dengchao Huang, Yao Zhang, Chun Wang & Long Yang. (2023) Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling. Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling.
Xinjie Liang, Wen Song & Pengfei Wei. (2023) Dynamic Job Shop Scheduling via Deep Reinforcement Learning. Dynamic Job Shop Scheduling via Deep Reinforcement Learning.
Jiang-Ping Huang, Liang Gao, Xin-Yu Li & Chun-Jiang Zhang. (2023) A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals. Computers & Industrial Engineering 185, pages 109650.
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Surabhi Singh, Shiwangi Singh, Alex Koohang, Anuj Sharma & Sanjay Dhir. (2023) Soft computing in business: exploring current research and outlining future research directions. Industrial Management & Data Systems 123:8, pages 2079-2127.
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Kaikai Zhu, Guiliang Gong, Ningtao Peng, Liqiang Zhang, Dan Huang, Qiang Luo & Xiaoqiang Li. (2023) Dynamic distributed flexible job-shop scheduling problem considering operation inspection. Expert Systems with Applications 224, pages 119840.
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XinYu LI, JiangPing HUANG, JiaHang LI, YuXin LI & Liang GAO. (2023) Research and development trend of intelligent shop dynamic scheduling. SCIENTIA SINICA Technologica 53:7, pages 1016-1030.
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Adilanmu Sitahong, Yiping Yuan, Junyan Ma, Yongxin Lu & Peiyin Mo. (2023) Effective and Interpretable Rule Mining for Dynamic Job-Shop Scheduling via Improved Gene Expression Programming with Feature Selection. Applied Sciences 13:11, pages 6631.
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Adilanmu Sitahong, Yiping Yuan, Ming Li, Junyan Ma, Zhiyong Ba & Yongxin Lu. (2022) Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling. Processes 11:1, pages 65.
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Alexandros S. Xanthopoulos & Dimitrios E. Koulouriotis. (2022) Simulation Study of Scheduling Heuristics for Parallel Machines with Sequence-Independent Setups. IFAC-PapersOnLine 55:10, pages 526-531.
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