인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This paper presents a study on the development of an optimal container stowage plan using rein- forcement learning to minimize the number of re-handles. Typically, container stowage plans are manually created by planners who consider factors such as destination, size, type, and weight of the cargo, making the process time-consuming and error-prone. To address this issue, we employed the DQN (Deep Q-N etwork) algorithm, a type of reinforcement learning, to establish an optimal container stowage plan. We proposed a method for defining state using the character- istics of the containers and compared it with traditional methods, using constraints such as desti- nation port and weight. The state definition method proposed in this study has the advantage of not exponentially increasing the number of states with the size of the stowage space by using characteristic values related to the loading status of each row. Initially, the performance of the DQN algorithm was verified using destination port as the only constraint. Subsequently, weight constraints were added to the state definition method that showed better performance. The study results show that the proposed method successfully established a stowage plan that satisfies all constraints in a 4×4 stowage space without causing re-handles, across nine test container lists.
본문·목차
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