One model packs thousands of items with Recurrent Conditional Query Learning

Li, Dongda; Gu, Zhaoquan; Wang, Yuexuan; Ren, Changwei; Lau, Francis C. M.

doi:10.1016/j.knosys.2021.107683

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…Please note that the above problem description and MDP is for a basic offline 3D bin packing. The instantiations of the MDP have been successfully applied for specific packing problems in some works [20,66]. Other variants of packing problems and their corresponding MDPs (e.g.…”

Section: Bin Packing Problemsmentioning

confidence: 99%

“…There are also some endeavours to simultaneously learn policies for selecting the item, its rotation and position. In specific, Li et al decompose the actions for combinations of item, rotation and position into sub-actions for each dimension, and train the respective policies by an actor-critic algorithm with generalised advantage estimation [66,105]. This method is evaluated on offline/online 2D and 3D-BPPs.…”

Section: Off-line Bppsmentioning

confidence: 99%

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A review on learning to solve combinatorial optimisation problems in manufacturing

Zhang

et al. 2023

IET Collab Intel Manufact

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An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state-of-the-art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges. K E Y W O R D Sbin packing, combinatorial optimisation, deep reinforcement learning, job shop scheduling, manufacturing systems, vehicle routing | INTRODUCTIONCombinatorial optimisation problems (COPs), as one important branch of mathematical optimisation, have practical applications in many fields, such as communication, transportation, manufacturing and aroused broad research in industrial engineering, computer science, and operations research. Due to the NP (non-deterministic polynomial-time) hardness, finding their optimal solutions is challenging. In specific, the discrete solution space in COPs renders the optimisation less efficient, without the guidance of gradient as in continuous optimisation. Meanwhile, the complexity of searching the (near-)optimal solution(s) among feasible solutions could exponentially increase as the problem scale grows. Classic methods, including exact algorithms and (meta-)heuristics, generally depend on massive expertise and tuning work to solve specific problems. They are Cong Zhang, Yaoxin Wu, and Yining Ma are equal contribution.

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Section: Bin Packing Problemsmentioning

confidence: 99%

Section: Off-line Bppsmentioning

confidence: 99%

A review on learning to solve combinatorial optimisation problems in manufacturing

Zhang

et al. 2023

IET Collab Intel Manufact

View full text Add to dashboard Cite

show abstract

“…Since there is no posture constraint, a part can be placed anywhere in the master surface with any angle, and the same batch of parts can have multiple orders, the solution space for the 2D irregular packing problem is very large. The packing problem is therefore an NP-complete (NP-C) problem (Zhang et al, 2022a;Li et al, 2022); that is, there is no effective polynomial algorithm that can identify an optimal solution in polynomial time. For NP-C problems, the existing solution is to find an approximate optimal solution using a heuristic algorithm.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The 2D irregular packing problem generates multiple subproblems, but the two main sub-problems are the geometric problem of the shape and the optimization of the placing sequence (Li et al, 2022). The two sub-problems are reviewed separately.…”

mentioning

confidence: 99%

Two-dimensional irregular packing problems: A review

Guo

et al. 2022

Front. Mech. Eng.

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Two-dimensional (2D) irregular packing problems are widespread in manufacturing industries such as shipbuilding, metalworking, automotive production, aerospace, clothing and furniture manufacturing. Research on 2D irregular packing problems is essential for improving material utilization and industrial automation. Much research has been conducted on this problem with significant research results and certain algorithms. The work has made important contributions to solving practical problems. This paper reviews recent advances in the domain of 2D irregular packing problems based on a variety of research papers. We first introduce the basic concept and research background of 2D irregular packing problems and then summarize algorithms and strategies that have been proposed for the problems in recent years. Conclusion summarize development trends and research hotspots of typical 2D irregular shape packing problems. We hope that this review could provide guidance for researchers in the field of 2D irregular packing.

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“…Agarwal et al [16] presented a new bin packing system, Jampacker, to achieve faster completion of the packing process. Li et al [17] proposed a recurrent conditional query learning (RCQL) method to solve 3D packing problems.…”

Section: Introductionmentioning

confidence: 99%

Multibox Three-Dimensional Packing Problems for Heterogeneous Extrudable Items

Zhao

Zhou

Wang

et al. 2022

Mathematical Problems in Engineering

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Aiming at the multibox 3D packing problem of heterogeneous extrudable items in e-commerce retail department stores, the optimization goal is to select appropriate packaging materials for packing all items in the order. On this basis, the number of packaging materials used is the least and the space utilization rate is high. A heuristic algorithm is designed. Firstly, the items are sorted in descending order according to volume, and then the items are boxed according to the rule of “layer generation.” In order to verify the feasibility and effectiveness of the algorithm, the data provided by a large domestic e-commerce platform are used to analyse and compare the space utilization of packaging materials under the conditions of no extrusion operation and extrusion operation. The feasibility and effectiveness of the algorithm in the packaging and delivery link in the storage field of e-commerce retail department stores are proved.

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One model packs thousands of items with Recurrent Conditional Query Learning

Cited by 18 publications

References 23 publications

A review on learning to solve combinatorial optimisation problems in manufacturing

A review on learning to solve combinatorial optimisation problems in manufacturing

Two-dimensional irregular packing problems: A review

Multibox Three-Dimensional Packing Problems for Heterogeneous Extrudable Items

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