{"id":11608,"date":"2026-09-06T15:25:20","date_gmt":"2026-09-06T07:25:20","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11608"},"modified":"2026-09-06T16:54:07","modified_gmt":"2026-09-06T08:54:07","slug":"jase-202612-35-017","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-017","title":{"rendered":"Research on Logistics Scheduling Model for Production Workshops Based on Deep Reinforcement Learning"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Yan Wang, Xiufeng Cao<\/i><\/a>, Xiaosuo Luo, and Bin Zhou<\/p>\n\n\n\n

School of Artificial Intelligence, Chongqing University of Education, Chongqing 400065, China<\/p>\n<\/div>\n\n\n\n

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Received: June 13, 2026
Accepted: August 14, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Comparison of the results of different reinforcement algorithms<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.017<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Aiming at solving difficult problems with traditional methods of logistics scheduling in production workshops, a logistics scheduling model based on deep reinforcement learning is studied[cite: 15]. The proposed reinforcement learning method based on the Markov Decision Process fully explores the impact of various factors within the production workshop on logistics scheduling[cite: 15]. Meanwhile, its correlation is analyzed, and the scheduling problem of the production workshop is solved through the double deep Q-network (DDQN) algorithm[cite: 15]. Multi-source heterogeneous data such as people, machines, processes, and the environment in the manufacturing workshop will be processed and analyzed, which can extract the logical data for model training in the production workshop and guide the intelligence of the production workshop[cite: 15]. Experimental results show that this method has better time allocation and higher efficiency, has better advantages than other algorithms, and is more adaptable to dynamic environments[cite: 15]. Through the comparison of 100 experimental results, the probability of obtaining the optimal solution of the model by this method reaches 92%, and the working time obtained is shorter[cite: 15].<\/p>\n\n\n\n

Keywords: Markov decision, deep reinforcement learning, production workshop, logistics scheduling, DDQN<\/em><\/p>\n\n\n\n

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