Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method

2017/08/20 by Haoyuan Hu, Xiaodong Zhang, Hu, Haoyuan +7 · 8 citations
Computer Science · Engineering · #Advanced Manufacturing and Logistics Optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Manufacturing Process and Optimization #Optimization and Packing Problems #cs.AI

paper · pdf · doi:10.48550/arxiv.1708.05930

7 pages, 1 figures

arxiv created 2017/08/20 · openalex publication_date 2017/08/20 · arxiv updated 2017/08/22 · openalex created_date 2017/08/31 · openalex updated_date 2026/07/28

Abstract

In this paper, a new type of 3D bin packing problem (BPP) is proposed, in which a number of cuboid-shaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.

Citations

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Cited by

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• \(X\)-evolve: Solution space evolution powered by large language models
• A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
• A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
• Towards VM Rescheduling Optimization Through Deep Reinforcement Learning
• DGA₂D: Directed Graph-Guided Automated Algorithm Design with Large Language Models
• A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem

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• Online 3D Bin Packing with Constrained Deep Reinforcement Learning