@inproceedings{589d77b8740649539ed589952a0bd890,
title = "Research on the Problem of 3D Bin Packing under Incomplete Information Based on Deep Reinforcement Learning",
abstract = "The Bin Packing Problem (BPP) in the logistics industry is a classic NP-hard problem. In practical applications, often only the size information of the current box can be obtained whereas getting the information of the subsequent boxes almost impossible. In consequence, an algorithm is very important for giving the packing position in the case of incomplete information. This paper used Deep Reinforcement Learning (DRL) algorithm and Monte Carlo Tree Search (MCTS), formed the state input shape for this problem to establish a model to solve the 3D bin packing problem under incomplete information. This model can achieve an average space utilization of 65\%. The study's results proved that the model can solve the packing problem under incomplete information and has certain practical benefits.",
keywords = "Bin packing problem, Deep Q-learning, Monte Carlo tree search",
author = "Yupeng Wu and Liya Yao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on E-Commerce and E-Management, ICECEM 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2021",
doi = "10.1109/ICECEM54757.2021.00016",
language = "English",
series = "Proceedings - 2021 International Conference on E-Commerce and E-Management, ICECEM 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "38--42",
booktitle = "Proceedings - 2021 International Conference on E-Commerce and E-Management, ICECEM 2021",
address = "United States",
}