Benchmarking Dynamic Three-Dimensional Bin Packing Problems Using Discrete-Event Simulation

Wang, Ran; Nguyễn, Trung Thành; Kavakeb, Shayan; Yang, Zaili; Li, Changhe

doi:10.1007/978-3-319-31153-1_18

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…To compare the performance of OnlineBPH, we implemented three other algorithms from the literature:  The online packing algorithm in [28] (Algorithm 1). This online heuristic is based on a layer-building approach;  Algorithm864, proposed in [16] -a static approximation packing heuristic -where items are sorted by non-increasing volume.…”

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Online Packing Heuristic for the Three-Dimensional Container Loading Problem in Dynamic Environments and the Physical Internet

Nguyễn

Bui

et al. 2017

Lecture Notes in Computer Science

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Section: Computational Resultsmentioning

confidence: 99%

“…Due to the lack of proper data for online 3D-CLP, we follow the approach described in [28] to generate test problems for the case of identical container packing problems. We generated 4 classes (I, II, III and IV) of instances.…”

Section: Benchmark Problemsmentioning

confidence: 99%

An Online Packing Heuristic for the Three-Dimensional Container Loading Problem in Dynamic Environments and the Physical Internet

Nguyễn

Bui

et al. 2017

Lecture Notes in Computer Science

Self Cite

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“…There have been several strategies in designing fast approximate algorithms, e.g., guided local search (Faroe, Pisinger, and Zachariasen 2003), greedy search (De Castro Silva, Soma, and Maculan 2003), and tabu search (Lodi, Martello, and Vigo 1999;Crainic, Perboli, and Tadei 2009). Similar strategy has also been adapted to Online BPP (Ha et al 2017;Wang et al 2016). In contrast, genetic algorithms leads to better solutions as a global, randomized search (Li, Zhao, and Zhang 2014;Takahara and Miyamoto 2005).…”

Section: Related Workmentioning

confidence: 99%

Online 3D Bin Packing with Constrained Deep Reinforcement Learning

Zhao

She

Zhu

et al. 2021

AAAI

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We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP). In our problem, the agent has limited information about the items to be packed into a single bin, and an item must be packed immediately after its arrival without buffering or readjusting. The item's placement also subjects to the constraints of order dependence and physical stability. We formulate this online 3D-BPP as a constrained Markov decision process (CMDP). To solve the problem, we propose an effective and easy-to-implement constrained deep reinforcement learning (DRL) method under the actor-critic framework. In particular, we introduce a prediction-and-projection scheme: The agent first predicts a feasibility mask for the placement actions as an auxiliary task and then uses the mask to modulate the action probabilities output by the actor during training. Such supervision and projection facilitate the agent to learn feasible policies very efficiently. Our method can be easily extended to handle lookahead items, multi-bin packing, and item re-orienting. We have conducted extensive evaluation showing that the learned policy significantly outperforms the state-of-the-art methods. A preliminary user study even suggests that our method might attain a human-level performance.

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“…Similar strategies have also been adapted to Online BPP works like [17,20,46,50]. Different from the offline setting, the size information of coming items is unknown for the agent to optimize the packing and the packing order can not be adjusted as well.…”

Section: Related Workmentioning

confidence: 99%

“…It makes Online BPP a much more challenging problem. Some works [17,46] have employed the hand-coded heuristics based on the human experience. Meanwhile, works like [50] have adopted deep reinforcement learning to learn how to pack things effectively through trials and error optimization.…”

Section: Related Workmentioning

confidence: 99%

Learning Practically Feasible Policies for Online 3D Bin Packing

Zhao¹,

Zhu²,

Xu³

et al. 2021

Preprint

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We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. In this problem, the items are delivered to the agent without informing the full sequence information. Agent must directly pack these items into the target bin stably without changing their arrival order, and no further adjustment is permitted. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP). We adopt deep reinforcement learning, in particular, the on-policy actorcritic framework, to solve this MDP with constrained action space. To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree. It attains a high analysis accuracy while reducing the computational complexity from O(N 2 ) to O(N log N ), making it especially suited for RL training. Second, we propose a decoupled packing policy learning for different dimensions of placement which enables high-resolution spatial discretization and hence high packing precision. Third, we introduce a reward function that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm. Furthermore, we provide a comprehensive discussion on several key implemental issues. The extensive evaluation demonstrates that our learned policy outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.

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Benchmarking Dynamic Three-Dimensional Bin Packing Problems Using Discrete-Event Simulation

Cited by 7 publications

References 31 publications

An Online Packing Heuristic for the Three-Dimensional Container Loading Problem in Dynamic Environments and the Physical Internet

An Online Packing Heuristic for the Three-Dimensional Container Loading Problem in Dynamic Environments and the Physical Internet

Online 3D Bin Packing with Constrained Deep Reinforcement Learning

Learning Practically Feasible Policies for Online 3D Bin Packing

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