Deep-Pack: A Vision-Based 2D Online Bin Packing Algorithm with Deep Reinforcement Learning

Kundu, Olyvia; Dutta, Samrat; Kumar, Sanjay

doi:10.1109/ro-man46459.2019.8956393

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…The recent study on on-line BPPs by deep learning may start from [106], in which a CNN-based Q network is used to learn the position policy in 2D-BPPs. Verma et al claim a first trial to solve online 3D-BPPs with real-world physical constraints.…”

Section: On-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: On-line Bppsmentioning

confidence: 99%

“…The recent study on on‐line BPPs by deep learning may start from [106], in which a CNN‐based Q network is used to learn the position policy in 2D‐BPPs. Verma et al.…”

Section: Bin Packing Problemsmentioning

confidence: 99%

A review on learning to solve combinatorial optimisation problems in manufacturing

Zhang

et al. 2023

IET Collab Intel Manufact

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“…The previous studies successfully implemented online packing processes. Some have employed the fine-tuned heuristics based on the product packing routine [ 16 , 17 , 18 ] and the others have actively used deep reinforcement learning for their methods to learn how to pack things effectively themselves through trials and errors [ 11 , 19 , 20 , 21 ]. However, due to the high computational load of online optimization or learning complexity, objects have been assumed to be boxes without rotations or 3D problem has been decomposed into multiple 2D problems by packing objects layer by layer.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, this task could be termed as 3D BPP depending on the dimensions being used for maximizing the space use [ 9 ]. Moreover, this task is an online BPP because the dimensions and orientations of objects are unknown before they are picked by the picker [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data

Hong

Kim

Lee

2020

Sensors

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This paper proposes a novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container. Existing object-packing systems have the limitations of requiring the exact information of objects in advance or assuming them as boxes. Thus, this paper is mainly focused on the following two points: (1) Real-time calculation of the dimensions and orientation of an object; (2) Online optimization of the object’s position in a container. The dimensions and orientation of the object are obtained using an RGB-D sensor when the object is picked by a manipulator and moved over a certain position. The optimal position of the object is calculated by recognizing the container’s available space using another RGB-D sensor and minimizing the cost function that is formulated by the available space information and the optimization criteria inspired by the way people place things. The experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.

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“…In view of the complexity of the combinatorial optimization problems, the DQN [24] combining the reinforcement learning function and the artificial neural network has demonstrated its strong performance, especially for the Maximum Cut [25], the Maximum Common Subgraph [26] and other combinatorial optimization problems. A deep reinforcement learning framework based on dual DQN has been proposed for an online two-dimensional packing study [27]. Although reinforcement learning has better performance than heuristic algorithms on the problem of combinatorial optimization, the performance is influenced by the amount of training data.…”

Section: B Previous Workmentioning

confidence: 99%

Hybrid Heuristic Algorithm Based On Improved Rules & Reinforcement Learning for 2D Strip Packing Problem

Zhu

2020

IEEE Access

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Deep-Pack: A Vision-Based 2D Online Bin Packing Algorithm with Deep Reinforcement Learning

Cited by 20 publications

References 21 publications

A review on learning to solve combinatorial optimisation problems in manufacturing

A review on learning to solve combinatorial optimisation problems in manufacturing

Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data

Hybrid Heuristic Algorithm Based On Improved Rules & Reinforcement Learning for 2D Strip Packing Problem

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