Brain-Inspired Experience Reinforcement Model for Bin Packing in Varying Environments

Zhang, Linli; Li, Dewei; Jia, Shuai; Shao, Haibin

doi:10.1109/tnnls.2022.3144515

Cited by 9 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use tree search to explore the solution space with complex constraints, and a convolutional neural network trained on historical data to guide tree pruning. Zhang et al [8] focuses on BPPs in different contexts and draws inspiration from how the human brain solves similar decision-making problems. They proposed a brain-inspired model called BERM that leverages empirical information and learning paradigms to make decisions in different environments, thereby providing an optimal decision process for BPP tasks across multiple environments.…”

Section: D Bin Packing Problemmentioning

confidence: 99%

Online 3D Packing Problem Based on Bi-Value Guidance

Qi¹,

Zhang²

2023

JCC

View full text Add to dashboard Cite

The online 3D packing problem has received increasing attention in recent years due to its practical value. However, the problem itself possesses some peculiar properties, such as sequential decision-making and the large size of the state space, which have made the use of reinforcement learning with Markov decision processes a popular approach for solving this problem. In this paper, we focus on the problem of high variance in value estimation caused by reward uncertainty in the presence of highly uncertain dynamics. To address this, proposed a solution based on auxiliary tasks and intrinsic rewards for the online 3D bin packing problem, guided by a binary-valued network, to assist the agent in learning the policy within the framework of actor-critic deep reinforcement learning. Specifically, the maintenance of two-valued networks and the utilization of multi-valued network estimates are employed to replace the original value estimates, aiming to provide better guidance for the learning of policy networks. Experimentally, it has been demonstrated that our model can achieve more robust learning and outperform previous works in terms of performance.

show abstract

Section: D Bin Packing Problemmentioning

confidence: 99%

Online 3D Packing Problem Based on Bi-Value Guidance

Qi¹,

Zhang²

2023

JCC

View full text Add to dashboard Cite

show abstract

“…Moreover, aside from the operational constraints, exact approaches have emerged as critical elements in addressing the 2D-SPP problem, showcasing a rich diversity over time. Particularly, recent advancements have seen the application of machine learning techniques, such as reinforcement learning, in supplementing or even substituting the traditional mathematical model-solving process [9,10]. However, the primary advantage of mathematical models lies in their transparency, allowing for a clear understanding of the decision-making process.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The objective function (10) aims to minimize the total number of cuts, where k belongs only to K i .…”

Section: Master Problem (Mp)mentioning

confidence: 99%

Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins

Liu,

Zhang,

Wang

et al. 2023

Mathematics

View full text Add to dashboard Cite

The two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to minimize the usage of height. Despite extensive academic exploration, applying 2D-SPP solutions in industrial settings remains challenging. Two significant issues, often overlooked in academia yet frequently encountered in industrial contexts, are the uncertain demand for items, exacerbated by the bullwhip effect, and the need for diverse types of strips to cater to varying customer needs. Our paper addresses this academia–industry gap by proposing a robust optimization model for the uncertain 2D-SPP with variable-sized bins, aiming to manage the demand fluctuations within a box uncertainty set framework. Additionally, we employ the contiguous one-dimensional relaxation technique in conjunction with column generation to tighten the lower bound of the problem, thereby augmenting solution accuracy. Furthermore, we leverage the Karush–Kuhn–Tucker (KKT) condition to transform the model into a more tractable form, subsequently leading to an exact solution. Based on datasets from a real-life plastic-cutting company, comprehensive experiments validate the effectiveness and efficiency of our proposed relaxation method and algorithm, showcasing the potential for an improved industrial application of 2D-SPP solutions.

show abstract

Deep Learning for Solving Loading, Packing, Routing, and Scheduling Problems

Elkefi,

Layeb

2023

Handbook of Formal Optimization

View full text Add to dashboard Cite

Brain-Inspired Experience Reinforcement Model for Bin Packing in Varying Environments

Cited by 9 publications

References 24 publications

Online 3D Packing Problem Based on Bi-Value Guidance

Online 3D Packing Problem Based on Bi-Value Guidance

Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins

Deep Learning for Solving Loading, Packing, Routing, and Scheduling Problems

Contact Info

Product

Resources

About