한국정보처리학회:학술대회논문집 (Annual Conference of KIPS)

한국정보처리학회 (Korea Information Processing Society)
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디지털 트윈을 이용한 항만 컨테이너 적재 최적화에 관한 연구

Study on Optimization of Port Container Operations Using Digital Twins

  • 김유경 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과) ;
  • 남정희 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과) ;
  • 양정은 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과) ;
  • 유채원 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과) ;
  • 하재복 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과) ;
  • 권혁준 (한국폴리텍대학 대전캠퍼스 메타버스콘텐츠과)
  • Kim Yoo-Kyung (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents) ;
  • Nam Jeong-Hee (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents) ;
  • Yang Jeong-Eun (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents) ;
  • Yu Chae-Won (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents) ;
  • Ha Jae-Bok (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents) ;
  • Kweon Hyeok-Jun (Korea Polytechnic IV Daejeon Campus, Department of Metaverse contents)
  • 발행 : 2024.10.31
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초록

This paper is a study on optimization of port container loading using digital twin technologies. It simulated and evaluated the performance of port container loading optimization using ML-agent's PPO (Proximal Policy Optimization) reinforcement learning algorithm. Through this, the study was conducted to help realize time and cost savings and energy optimization through efficient operation of containers in ports. In this algorithm, optimization was performed through the reinforcement learning process at 375,000 times.

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과제정보

본 논문은 해양수산부 실무형해상물류 일자리 지원사업(스마트해상물류 × ICT멘토링)을 통해 수행한 ICT멘토링 프로젝트 결과물입니다.

참고문헌

  1. Huang, S., et al, "The 37 Implementation Details of Proximal Policy Optimization,"in ICLR Blog Track, 2022.
  2. Chloe Ching-Yun Hsu, undefined. Celestine Mendler-Dunner, undefined. Moritz Hardt, "Revisiting Design Choices in Proximal Policy Optimization," 2020.
  3. Arthur Juliani, et al, "Unity: A General Platform for Intelligent Agents," 2020.