20.500.12556/DKUM-97086 An improved deep reinforcement learning approach: a case study for optimisation of berth and yard scheduling for bulk cargo terminal The cornerstone of port production operations is ship handling, necessitating judicious allocation of diverse production resources to enhance the efficiency of loading and unloading operations. This paper introduces an optimisation method based on deep reinforcement learning to schedule berths and yards at a bulk cargo terminal. A Markov Decision Process model is formulated by analysing scheduling processes and unloading operations in bulk port imports business. The study presents an enhanced reinforcement learning algorithm called PS-D3QN (Prioritised Experience Replay and Softmax strategy-based Dueling Double Deep Q-Network), amalgamating the strengths of the Double DQN and Dueling DQN algorithms. The proposed solution is evaluated using actual port data and benchmarked against the other two algorithms mentioned in this paper. The numerical experiments and comparative analysis substantiate that the PS-D3QN algorithm significantly enhances the efficiency of berth and yard scheduling in bulk terminals, reduces the cost of port operation, and eliminates errors associated with manual scheduling. The algorithm presented in this paper can be tailored to address scheduling issues in the fields of production and manufacturing with suitable adjustments, including problems like the job shop scheduling problem and its extensions. bulk cargo terminal scheduling optimisation Markov decision process (MDP) model deep reinforcement learning prioritised experience replay and softmax strategy-based dueling double deep Q-network pristanišča optimizacija nalaganje razlaganje kontejnerski terminali algoritmi globoko okrepljeno 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 2026-02-17 13:07:35 2026-02-17 13:07:35 2026-03-11 08:55:46 0000-00-00 00:00:00 2023 0 0 str. 303-316 no. 3 Vol. 18 Sep. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-08-22 2023-11-07 2023-11-19 626/627.093:004.85 268736003 10.14743/apem2023.3.474 1854-6250 APEM18-3_303-316.pdf APEM18-3_303-316.pdf 1 F039E9B5EAEC22F6F2A834E5A9BC62A7 49c8918945c54d73c8590e1cf7f5409f8682c5b3c063685289c2ec5cf6ee3cea d142aa48-0bf9-11f1-aedc-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203409 https://apem-journal.org/Archives/2023/APEM18-3_303-316.pdf 0 daaf5ea5-0bf9-11f1-aedc-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203410 Fakulteta za strojništvo Advances in production engineering & management 0 0 0