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International Journal of Production Research Volume 59, 2021 - Issue 9
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Articles

Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction

Sungbum JunSchool of Industrial Engineering, Purdue University, West Lafayette, IN, USACorrespondence[email protected]
&
Seokcheon LeeSchool of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Pages 2838-2856 | Received 25 Oct 2019, Accepted 05 Mar 2020, Published online: 23 Mar 2020

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Zhun Xu, Liyun Xu, Xufeng Ling & Beikun Zhang. (2023) Data-driven hierarchical learning and real-time decision-making of equipment scheduling and location assignment in automatic high-density storage systems. International Journal of Production Research 61:21, pages 7333-7352.
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Jens Heger & Thomas Voss. (2023) Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning. International Journal of Production Research 61:1, pages 147-161.
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Chen-Fu Chien, Yu-Bin Lan, Kanchana Sethanan & Chia-Ching Peng. (2024) Digital system for dynamic container loading with neural network-based memory exploiting hybrid genetic algorithm for carbon reduction. Computers & Industrial Engineering 191, pages 110149.
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Chew Wye Chan, Boon Ping Gan & Wentong Cai. (2023) Combining Time Series Data and Snapshot Data for Situation Aware Dispatching in Semiconductor Manufacturing. Combining Time Series Data and Snapshot Data for Situation Aware Dispatching in Semiconductor Manufacturing.
Anran Zhao, Peng Liu, Yunfeng Li, Zheyu Xie, Longhao Hu & Haoyuan Li. (2023) Real-Time Selection System of Dispatching Rules for the Job Shop Scheduling Problem. Machines 11:10, pages 921.
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Jianxin Fang, Brenda Cheang & Andrew Lim. (2023) Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey. Sustainability 15:17, pages 13012.
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Jing Wang. (2023) Modeling and Analysis of Psychological Change and Adaptability of College Students Based on Machine Learning as an Infrastructure to a Smart City. Journal of Testing and Evaluation 51:3, pages 1650-1660.
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Biao Zhang, Lei-lei Meng, Chao Lu & Jun-qing Li. (2023) Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling. Applied Soft Computing 138, pages 110187.
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Adriana Mexicano, Jesús C. Carmona-Frausto, Pascual N. Montes-Dorantes, Salvador Cervantes, José-Antonio Cervantes & Ricardo Rodríguez. 2023. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 86 95 .
Cen Gao, Pu-Huai Lu, Wei-Min Ye, Zhang-Rong Liu, Qiong Wang & Yong-Gui Chen. (2022) Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability. Environmental Science and Pollution Research 30:13, pages 35872-35890.
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Cristiane Ferreira, Gonçalo Figueira & Pedro Amorim. (2022) Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning. Omega 111, pages 102643.
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Liping Zhang, Yifan Hu, Chuangjian Wang, Qiuhua Tang & Xinyu Li. (2022) Effective dispatching rules mining based on near-optimal schedules in intelligent job shop environment. Journal of Manufacturing Systems 63, pages 424-438.
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Ran Ma, Lu Zhang & Yuzhong Zhang. (2022) A best possible algorithm for an online scheduling problem with position-based learning effect. Journal of Industrial and Management Optimization 18:6, pages 3989.
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Shruti Gupta, Rajani Kumari & Rishi Pal Singh. (2021) Lunar cycle inspired PSO for single machine total weighted tardiness scheduling problem. Evolutionary Intelligence 14:3, pages 1355-1366.
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Liping Zhang, Yifan Hu, Qiuhua Tang, Jie Li & Zhixiong Li. (2021) Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty. Sensors 21:14, pages 4836.
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Shiyun Li, Tianzong Yu, Xu Cao, Zhi Pei, Wenchao Yi, Yong Chen & Ruifeng Lv. (2021) Machine learning‐based scheduling: a bibliometric perspective. IET Collaborative Intelligent Manufacturing 3:2, pages 131-146.
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Shengluo Yang, Zhigang Xu & Junyi Wang. (2021) Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning. Sensors 21:3, pages 1019.
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Ye Gu, Shuyou Zhang & Lemiao Qiu. (2021) Digital Twin Driven Requirement Conversion in Smart Customized Design. IEEE Access 9, pages 64414-64426.
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