| Title: | An improved deep reinforcement learning approach: a case study for optimisation of berth and yard scheduling for bulk cargo terminal |
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| Authors: | ID Ai, T. (Author)
ID Huang, L. (Author)
ID Song, R. J. (Author)
ID Huang, H. F. (Author)
ID Jiao, F. (Author)
ID Ma, W. G. (Author) |
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| Files: |  APEM18-3_303-316.pdf (1019,21 KB)
MD5: F039E9B5EAEC22F6F2A834E5A9BC62A7
 https://apem-journal.org/Archives/2023/APEM18-3_303-316.pdf
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | 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 |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 22.08.2023 |
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| Article acceptance date: | 07.11.2023 |
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| Publication date: | 19.11.2023 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 303-316 |
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| Numbering: | Vol. 18, no. 3 |
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| PID: | 20.500.12556/DKUM-97086  |
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| UDC: | 626/627.093:004.85 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 268736003 |
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| DOI: | 10.14743/apem2023.3.474 |
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| Copyright: | 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. |
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| Publication date in DKUM: | 17.02.2026 |
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| Views: | 319 |
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| Downloads: | 3 |
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| Metadata: |    |
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| Categories: | Misc.
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