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항만 물류 환경에서 강화학습 기반 컨테이너 다단 적재 모델링 방법
장우석(Woo-Seok Jang),이효준(Hyo-June Lee),서민성(Min-Seoung Seo),이성진(Seong-Jin Lee),김동규(Dong-Gyu Kim) 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.4
In a port logistics environment, containers are stacked in multiple layers to maximize the use of limited space. To handle the lower-layer cargo in a multi-layer stacking situation, the cargo stacked on top must be moved to another location. This process is called container re-handling, and as it incurs costs, container terminal operators aim to minimize re-handling. This paper proposes a reinforcement learning model that recommends and selects loading positions to minimize re-handling while stacking containers in multiple layers. The proposed reinforcement learning model optimizes the loading order by considering the yard departure date of the containers, ensuring that the containers in the lower layer do not depart before those in the upper layer. This can reduce logistics costs and time during the container storage process.