| 제목 | 버퍼 활용 무작위 빈 패킹 문제를 위한 강화학습-휴리스틱 결합 모델 |
|---|---|
| Title | Reinforcement Learning with Heuristics for Buffer-Utilized Random Bin Packing Problem |
| 저자 |
김민지 (서울대학교 컴퓨터공학부, 투모로 로보틱스) 이강훈 (서울대학교 협동과정 인공지능전공, 투모로 로보틱스) 장병탁* (서울대학교 컴퓨터공학부, 서울대학교 AI 연구원, 투모로 로보틱스) |
| Author |
Minji Kim(Department of Computer Science and Engineering, Seoul National University, Tommoro Robotics) Ganghun Lee(Interdisciplinary Program in Artificial Intelligence, Seoul National University, Tommoro Robotics) Byoung-Tak Zhang*(Department of Computer Science and Engineering, Seoul National University, Interdisciplinary Program in Artificial Intelligence, Seoul National University, Tommoro Robotics) |
| Bibliography | Journal of Logistics Science & Technology, 4(1),1~18, 2023, |
| DOI | |
| Key Words | Buffer-utilized bin packing, Reinforcement learning, Heuristics, Value estimation, Uncertainty handling |
| Abstract | In this paper, we propose a reinforcement learning algorithm combined with heuristics to solve the bin packing problem (BPP) where the objects are randomly given and once placed objects cannot be moved, but the small load buffer can be utilized. This setting resembles the loading problem which has not been resolved despite of logistics automation. Since heuristics can be rapidly optimized by human intuition, and reinforcement learning is highly responsive to the environment, the combined method is highly available in the real-world industries. When the model learned through reinforcement learning determines the optimal object among the new object and objects in the buffer, then defined heuristics find optimal position and orientation for placement of the selected object. Through experiments, we verified the effectiveness by comparing the loading efficiency between the heuristic-only system and the combined system presented in this study. |
| PDF download | JM_4[1]-P1~18.pdf |