文献类型：期刊文献

英文题名：A Flexible Reinforced Bin Packing Framework with Automatic Slack Selection

作者：Yang, Ting[1];Luo, Fei[1];Fuentes, Joel[2];Ding, Weichao[1];Gu, Chunhua[1]

机构：[1]East China Univ Sci & Technol, Sch Informat & Engn, Shanghai 200237, Peoples R China;[2]Univ Bio Bio, Dept Comp Sci & Informat Technol, Chillan, Chile

年份：2021

卷号：2021

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20212310459067);WOS:【SCI-EXPANDED(收录号:WOS:000669014400010)】；

基金：This work was supported by the National Natural Science Foundation of China (no. 61472139) and the Shanghai 2020 Action Plan of Technological Innovation (no. 20dz1201400).

语种：英文

外文关键词：Radial basis function networks - Reinforcement learning - Eigenvalues and eigenfunctions - Mapping - Reinforcement

摘要：The slack-based algorithms are popular bin-focus heuristics for the bin packing problem (BPP). The selection of slacks in existing methods only consider predetermined policies, ignoring the dynamic exploration of the global data structure, which leads to nonfully utilization of the information in the data space. In this paper, we propose a novel slack-based flexible bin packing framework called reinforced bin packing framework (RBF) for the one-dimensional BPP. RBF considers the RL-system, the instance-eigenvalue mapping process, and the reinforced-MBS strategy simultaneously. In our work, the slack is generated with a reinforcement learning strategy, in which the performance-driven rewards are used to capture the intuition of learning the current state of the container space, the action is the choice of the packing container, and the state is the remaining capacity after packing. During the construction of the slack, an instance-eigenvalue mapping process is designed and utilized to generate the representative and classified validate set. Furthermore, the provision of the slack coefficient is integrated into MBS-based packing process. Experimental results show that, in comparison with fit algorithms, MBS and MBS', RBF achieves state-of-the-art performance on BINDATA and SCH_WAE datasets. In particular, it outperforms its baseline MBS and MBS', averaging the number increase of optimal solutions of 189.05% and 27.41%, respectively.