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Special issue: Artificial Intelligence in Manufacturing and Logistics Systems: Algorithms, Applications, and Case Studies

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

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Pages 2822-2840 | Received 07 Oct 2018, Accepted 11 Jan 2020, Published online: 24 Feb 2020

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Violetta Giada Cannas, Maria Pia Ciano, Mattia Saltalamacchia & Raffaele Secchi. (2024) Artificial intelligence in supply chain and operations management: a multiple case study research. International Journal of Production Research 62:9, pages 3333-3360.
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Elad Shufan, Tal Grinshpoun, Ehud Ikar & Hagai Ilani. (2023) Reentrant flow shop with identical jobs and makespan criterion. International Journal of Production Research 61:1, pages 183-197.
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Namyong Kim, Stephane Barde, Kiwook Bae & Hayong Shin. (2023) Learning per-machine linear dispatching rule for heterogeneous multi-machines control. International Journal of Production Research 61:1, pages 162-182.
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Shengluo Yang & Zhigang Xu. (2022) Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing. International Journal of Production Research 60:16, pages 4936-4953.
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Arunmozhi Manimuthu, V. G. Venkatesh, V. Raja Sreedharan & Venkatesh Mani. (2022) Modelling and analysis of artificial intelligence for commercial vehicle assembly process in VUCA world: a case study. International Journal of Production Research 60:14, pages 4529-4547.
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Articles from other publishers (17)

Yang Yang, Bin Zhang & Cheng-Hung Wu. (2024) Decision synthesis framework approach for dynamic dispatching and preventive maintenance in large flexible manufacturing systems. Advanced Engineering Informatics 61, pages 102417.
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Jin-Seop Lee, Tae-Hyun Kim, Sang-Hwan Jeon, Sung-Hyun Park, Sang-Hi Kim, Eun-Ho Lee & Jee-Hyong Lee. (2024) Automation of trimming die design inspection by zigzag process between AI and CAD domains. Engineering Applications of Artificial Intelligence 127, pages 107283.
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Chen-Fu Chien, Hans Ehm, John W. Fowler, Karl G. Kempf, Lars Mönch & Cheng-Hung Wu. (2023) Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains. IEEE Transactions on Semiconductor Manufacturing 36:4, pages 560-569.
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Bin Zhang & Cheng-Hung Wu. (2023) Joint Dynamic Dispatching and Preventive Maintenance for Unrelated Parallel Machines With Equipment Health Considerations. IEEE Transactions on Semiconductor Manufacturing 36:4, pages 578-589.
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Christian John Immanuel S. Boydon, Bin Zhang & Cheng-Hung Wu. (2023) Deep Learning Agents for Efficient Dynamic Production Control in Semiconductor Manufacturing. Deep Learning Agents for Efficient Dynamic Production Control in Semiconductor Manufacturing.
Benjamin Kovács, Pierre Tassel & Martin Gebser. (2023) Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming. Optimizing Dispatching Strategies for Semiconductor Manufacturing Facilities with Genetic Programming.
Saumyaranjan Sahoo, Satish Kumar, Mohammad Zoynul Abedin, Weng Marc Lim & Suresh Kumar Jakhar. (2022) Deep learning applications in manufacturing operations: a review of trends and ways forward. Journal of Enterprise Information Management 36:1, pages 221-251.
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Jose Arnaldo Barra Montevechi, Gustavo Teodoro Gabriel, Afonso Teberga Campos, Carlos Henrique dos Santos, Fabiano Leal & Michael E. F. H. S. Machado. (2022) Using Generative Adversarial Networks to Validate Discrete Event Simulation Models. Using Generative Adversarial Networks to Validate Discrete Event Simulation Models.
Bakass Assiya, Tarik Agouti, Jihad Zahir, A. Ait-Mlouk & Mohammed El Adnani. (2022) Big data and Intelligent decision Making: Approaches and Applications. Big data and Intelligent decision Making: Approaches and Applications.
Marcel Panzer, Benedict Bender & Norbert Gronau. (2022) Neural agent-based production planning and control: An architectural review. Journal of Manufacturing Systems 65, pages 743-766.
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Guohui Zhang, Xixi Lu, Xing Liu, Litao Zhang, Shiwen Wei & Wenqiang Zhang. (2022) An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown. Expert Systems with Applications 203, pages 117460.
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Chunquan Li, Yaqiong Chen & Yuling Shang. (2022) A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal 29, pages 101021.
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Shihong Liu, Shichang Du, Lifeng Xi, Yiping Shao & Delin Huang. (2022) A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems. Sensors 22:6, pages 2409.
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Maria Grazia Marchesano, Guido Guizzi, Valentina Popolo & Giuseppe Converso. (2022) Dynamic scheduling of a due date constrained flow shop with Deep Reinforcement Learning. IFAC-PapersOnLine 55:10, pages 2932-2937.
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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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Maria Grazia Marchesano, Guido Guizzi, Liberatina Carmela Santillo & Silvestro Vespoli. (2021) A Deep Reinforcement Learning approach for the throughput control of a Flow-Shop production system. IFAC-PapersOnLine 54:1, pages 61-66.
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Alican Dogan & Derya Birant. (2020) Machine Learning and Data Mining in Manufacturing. Expert Systems with Applications, pages 114060.
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