Relevant bibliographies by topics / Reinforcement learning. Production scheduling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Reinforcement learning. Production scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Reinforcement learning. Production scheduling"

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Lee, Seunghoon, Yongju Cho, and Young Hoon Lee. "Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning." Sustainability 12, no. 20 (2020): 8718. http://dx.doi.org/10.3390/su12208718.

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold pr
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Waschneck, Bernd, André Reichstaller, Lenz Belzner, et al. "Optimization of global production scheduling with deep reinforcement learning." Procedia CIRP 72 (2018): 1264–69. http://dx.doi.org/10.1016/j.procir.2018.03.212.

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Hubbs, Christian D., Can Li, Nikolaos V. Sahinidis, Ignacio E. Grossmann, and John M. Wassick. "A deep reinforcement learning approach for chemical production scheduling." Computers & Chemical Engineering 141 (October 2020): 106982. http://dx.doi.org/10.1016/j.compchemeng.2020.106982.

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Wang, Yi-Chi, and John M. Usher. "Application of reinforcement learning for agent-based production scheduling." Engineering Applications of Artificial Intelligence 18, no. 1 (2005): 73–82. http://dx.doi.org/10.1016/j.engappai.2004.08.018.

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Guo, Fang, Yongqiang Li, Ao Liu, and Zhan Liu. "A Reinforcement Learning Method to Scheduling Problem of Steel Production Process." Journal of Physics: Conference Series 1486 (April 2020): 072035. http://dx.doi.org/10.1088/1742-6596/1486/7/072035.

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Shi, Daming, Wenhui Fan, Yingying Xiao, Tingyu Lin, and Chi Xing. "Intelligent scheduling of discrete automated production line via deep reinforcement learning." International Journal of Production Research 58, no. 11 (2020): 3362–80. http://dx.doi.org/10.1080/00207543.2020.1717008.

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Kardos, Csaba, Catherine Laflamme, Viola Gallina, and Wilfried Sihn. "Dynamic scheduling in a job-shop production system with reinforcement learning." Procedia CIRP 97 (2021): 104–9. http://dx.doi.org/10.1016/j.procir.2020.05.210.

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Han, Guo, and Su. "A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem." Algorithms 12, no. 11 (2019): 222. http://dx.doi.org/10.3390/a12110222.

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The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitatio
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Zhou, Tong, Dunbing Tang, Haihua Zhu, and Liping Wang. "Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory." IEEE Access 9 (2021): 752–66. http://dx.doi.org/10.1109/access.2020.3046784.

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Zhang, Zhicong, Kaishun Hu, Shuai Li, Huiyu Huang, and Shaoyong Zhao. "Chip Attach Scheduling in Semiconductor Assembly." Journal of Industrial Engineering 2013 (March 26, 2013): 1–11. http://dx.doi.org/10.1155/2013/295604.

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Chip attach is the bottleneck operation in semiconductor assembly. Chip attach scheduling is in nature unrelated parallel machine scheduling considering practical issues, for example, machine-job qualification, sequence-dependant setup times, initial machine status, and engineering time. The major scheduling objective is to minimize the total weighted unsatisfied Target Production Volume in the schedule horizon. To apply Q-learning algorithm, the scheduling problem is converted into reinforcement learning problem by constructing elaborate system state representation, actions, and reward functi
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Dissertations / Theses on the topic "Reinforcement learning. Production scheduling"

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Wang, Yi-Chi. "Application of reinforcement learning to multi-agent production scheduling." Diss., Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-10212003-094739.

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Stigenberg, Jakob. "Scheduling using Deep Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284506.

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As radio networks have continued to evolve in recent decades, so have theircomplexity and the difficulty in efficiently utilizing the available resources. Ina cellular network, the scheduler controls the allocation of time, frequencyand spatial resources to users in both uplink and downlink directions. Thescheduler is therefore a key component in terms of efficient usage of networkresources. Although the scope and characteristics of available resources forschedulers are well defined in network standards, e.g. Long-Term Evolutionor New Radio, its real implementation is not. Most previous work f
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Hanus, Deborah. "Smart scheduling : optimizing Tilera's process scheduling via reinforcement learning." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85423.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 45-48).<br>As multicore processors become more prevalent, system complexities are increasing. It is no longer practical for an average programmer to balance all of the system constraints to ensure that the system will always perform optimally. One apparent solution to managing these resources efficiently is to design a self-aware system that utilizes machine learning to optimally manage it
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Rogers, Keith Eric. "Scheduling of costly measurements for state estimation using reinforcement learning." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/28216.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999.<br>Includes bibliographical references (p. 257-263).<br>There has long been a significant gap between the theory and practice of measurement scheduling for state estimation problems. Theoretical papers tend to deal rigorously with small-scale, linear problems using methods that are well-grounded in optimization theory. Practical applications deal with high-dimensional, nonlinear problems using heuristic policies. The work in this thesis attempts to bridge that gap by using reinforcement learning
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von, Hacht Johan, and David Johansson. "Reinforcement Learning Applied to Select Traffic Scheduling Method in Intersections." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260080.

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Effective scheduling of traffic is vital for a city to function optimally. For high-density traffic in urban areas, intersections and how they schedule traffic plays an integral part in preventing congestion. Current traffic light scheduling methods predominantly consist of using fixed time intervals to schedule traffic, a method not taking advantage of the technological leaps of recent years. With the unpredictable characteristic of traffic and urban population ever-expanding, conventional traffic scheduling becomes less effective due to them being nonadaptive. Therefore, the study sought out
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Dhandayuthapani, Sumithra. "Automatic selection of dynamic loop scheduling algorithms for load balancing using reinforcement learning." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-06292004-144402.

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Baheri, Betis. "MARS: Multi-Scalable Actor-Critic Reinforcement Learning Scheduler." Kent State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=kent1595039454920637.

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Werfel, Justin (Justin Keith) 1977. "Neural network models for zebra finch song production and reinforcement learning." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86791.

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Burton, Scott H. "Coping with the Curse of Dimensionality by Combining Linear Programming and Reinforcement Learning." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/559.

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Reinforcement learning techniques offer a very powerful method of finding solutions in unpredictable problem environments where human supervision is not possible. However, in many real world situations, the state space needed to represent the solutions becomes so large that using these methods becomes infeasible. Often the vast majority of these states are not valuable in finding the optimal solution. This work introduces a novel method of using linear programming to identify and represent the small area of the state space that is most likely to lead to a near-optimal solution, significantl
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Doltsinis, Stefanos. "A decision support system for production ramp-up : a reinforcement learning approach." Thesis, University of Nottingham, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.755814.

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New technologies have been developing rapidly in the last decades. Enterprises require incorporating these technologies in the development of new products. That creates a high pace of new product flow with an increasingly small life cycle. In order to support the fast pace, manufacturing lines have to adapt to the new product requirements as fast as possible. Production ramp-up is a phase in the manufacturing line that has a significant role on the required time to market but currently constitutes a bottleneck in the manufacturing process. Studies have focused on analysing ramp-up and defining
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Books on the topic "Reinforcement learning. Production scheduling"

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1934-, Gardner David C., ed. ACT! 2.0 for Windows: The visual learning guide. Prima Pub., 1995.

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Zhang, Wei. Reinforcement learning for job-shop scheduling. 1996.

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Gardner, David C., and Grace Joely Beatty. Act! 2.0 for Windows: The Visual Learning Guide. Premier, 1994.

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Book chapters on the topic "Reinforcement learning. Production scheduling"

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Marchesano, Maria Grazia, Guido Guizzi, Liberatina Carmela Santillo, and Silvestro Vespoli. "Dynamic Scheduling in a Flow Shop Using Deep Reinforcement Learning." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85874-2_16.

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Knowles, Michael, David Baglee, and Stefan Wermter. "Reinforcement Learning for Scheduling of Maintenance." In Research and Development in Intelligent Systems XXVII. Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-130-1_31.

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Tan, Yingcong. "Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning." In Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89656-4_36.

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Wu, Qing, Zhiwei Wu, Yuehui Zhuang, and Yuxia Cheng. "Adaptive DAG Tasks Scheduling with Deep Reinforcement Learning." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05054-2_37.

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Lam, Jason T., François Rivest, and Jean Berger. "Deep Reinforcement Learning for Multi-satellite Collection Scheduling." In Theory and Practice of Natural Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34500-6_13.

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Liang, Sisheng, Zhou Yang, Fang Jin, and Yong Chen. "Data Centers Job Scheduling with Deep Reinforcement Learning." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_68.

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Yang, Tingting, and Xuemin (Sherman) Shen. "Intelligent Transmission Scheduling Based on Deep Reinforcement Learning." In Mission-Critical Application Driven Intelligent Maritime Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4412-5_3.

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Yao, Zhenjie, Lan Chen, and He Zhang. "Deep Reinforcement Learning for Job Scheduling on Cluster." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86380-7_50.

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Klöser, Sebastian, Sebastian Kotstein, Robin Reuben, Timo Zerrer, and Christian Decker. "Deep Reinforcement Learning for IoT Interoperability." In Advances in Automotive Production Technology – Theory and Application. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62962-8_23.

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Riezebos, Jan, and Jannes Slomp. "The Shop Floor Scheduling Game." In Simulation Games and Learning in Production Management. Springer US, 1995. http://dx.doi.org/10.1007/978-1-5041-2870-4_11.

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Conference papers on the topic "Reinforcement learning. Production scheduling"

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Waschneck, Bernd, Andre Reichstaller, Lenz Belzner, et al. "Deep reinforcement learning for semiconductor production scheduling." In 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC). IEEE, 2018. http://dx.doi.org/10.1109/asmc.2018.8373191.

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Riemer-Sorensen, Signe, and Gjert H. Rosenlund. "Deep Reinforcement Learning for Long Term Hydropower Production Scheduling." In 2020 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2020. http://dx.doi.org/10.1109/sest48500.2020.9203208.

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Arinez, Jorge, Xinyan Ou, and Qing Chang. "Gantry Scheduling for Two-Machine One-Buffer Composite Work Cell by Reinforcement Learning." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2854.

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In this paper, a manufacturing work cell with a gantry that is in charge of moving materials/parts between machines and buffers is considered. With the effect of the gantry movement, the system performance becomes quite different from traditional serial production lines. In this paper, reinforcement learning is used to develop a gantry scheduling policy in order to improve system production. The gantry learns to take proper actions under different situations to reduce system production loss by using Q-Learning algorithm and finds the optimal moving policy. A two-machine one-buffer work cell wi
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Seito, Takanari, and Satoshi Munakata. "Production Scheduling based on Deep Reinforcement Learning using Graph Convolutional Neural Network." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009095207660772.

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Lang, Sebastian, Fabian Behrendt, Nico Lanzerath, Tobias Reggelin, and Marcel Muller. "Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9383997.

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Zavyalova, D., and V. Drozdova. "5G Scheduling using Reinforcement Learning*." In 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2020. http://dx.doi.org/10.1109/fareastcon50210.2020.9271421.

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Gaafar, Mohamed, Mahdi Shaghaghi, Raviraj S. Adve, and Zhen Ding. "Reinforcement Learning for Cognitive Radar Task Scheduling." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048892.

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Remya, S., Jenifer Mariam Johnson, and TP Imthias Ahamed. "Short Term Hydrothermal Scheduling Using Reinforcement Learning." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951416.

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Mattila, Ville, and Kai Virtanen. "Scheduling fighter aircraft maintenance with reinforcement learning." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6147962.

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Rosello, Marc Molla. "Multi-path Scheduling with Deep Reinforcement Learning." In 2019 European Conference on Networks and Communications (EuCNC). IEEE, 2019. http://dx.doi.org/10.1109/eucnc.2019.8802063.

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You might also be interested in the extended bibliographies on the topic 'Reinforcement learning. Production scheduling' for particular source types:
Journal articles Dissertations / Theses
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