Deep Q-network and knowledge jointly-driven ship operational efficiency optimization in a seaport

Wenqiang Guo, Xinyu Zhang, Ying-En Ge and Yuquan Du

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 197, issue C

Abstract: This study addresses a ship operational efficiency optimization problem for a seaport. Given the number of planned inbound ships, the problem optimizes the inbound sequence of all ships and their speed profiles at different inbound stages. A mixed-integer nonlinear programming model is presented to minimize both the total time of ships’ port entry process (TTEP) and the total fuel consumption (TFC) of the ships. A novel deep Q-network and knowledge jointly-driven cooperative metaheuristic algorithm (DQNKD-CMA) is designed to solve the model. Experimental results based on real scenarios set in Tianjin Port demonstrate that DQNKD-CMA exhibits favorable performance in solving the problem. The proposed method improves ship inbound efficiency and reduces carbon emissions through operational measures, providing a cost-effective alternative to energy-saving equipment and alternative fuels for ship emission mitigation. This study offers a significant set of implications to shipping and port operators who face new carbon emission reduction challenges.

Keywords: Ship operational efficiency; Port Call Optimization; Deep Q-network; Knowledge; Cooperative metaheuristic algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104046

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