20.500.12556/DKUM-97033 Real-time scheduling for dynamic workshops with random new job insertions by using deep reinforcement learning Dynamic real-time workshop scheduling on job arrival is critical for effective production. This study proposed a dynamic shop scheduling method integrating deep reinforcement learning and convolutional neural network (CNN). In this method, the spatial pyramid pooling layer was added to the CNN to achieve effective dynamic scheduling. A five-channel, two-dimensional matrix that expressed the state characteristics of the production system was used to capture the state of the real-time production of the workshop. Adaptive scheduling was achieved by using a reward function that corresponds to the minimum total tardiness, and the common production dispatching rules were used as the action space. The experimental results revealed that the proposed algorithm achieved superior optimization capabilities with lower time cost than that of the genetic algorithm and could adaptively select appropriate dispatching rules based on the state features of the production system. real-time scheduling machine learning deep reinforcement learning DRL spatial pyramid pooling layer artificial neural networks ANN convolutional neural networks CNN konvolucijske nevronske mreže umetne nevronske mreže globoko učenje true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-02-12 14:40:47 2026-02-12 14:40:48 2026-02-13 03:12:13 0000-00-00 00:00:00 2023 0 0 str. 137-151 no. 2 Vol. 18 Jun. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-12-07 2023-06-25 2023-07-21 004.8 268213507 10.14743/apem2023.2.462 1854-6250 APEM18-2_137-151.pdf APEM18-2_137-151.pdf 1 69F2274AC3C2AB253B699FDEE617E60B 85b17191d056c057eaa49cc3b4d161c81b3b6e71f97c3189b6e73577abf425b2 47b1e9ff-0819-11f1-93cb-00155d00019a https://dk.um.si/Dokument.php?lang=slv&id=203272 https://apem-journal.org/Archives/2023/Abstract-APEM18-2_137-151.html 0 711699c1-0819-11f1-93cb-00155d00019a https://dk.um.si/Dokument.php?lang=slv&id=203273 Fakulteta za strojništvo Advances in production engineering & management 0 0 0