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

IF 8.3 1区工程技术 Q1 ECONOMICS

Transportation Research Part E-Logistics and Transportation Review Pub Date : 2025-03-04 DOI:10.1016/j.tre.2025.104046

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

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引用次数: 0

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.

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来源期刊

Transportation Research Part E-Logistics and Transportation Review 工程技术-工程：土木

CiteScore

16.20

自引率

16.00%

发文量

285

审稿时长

62 days

期刊介绍： Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.