Online 3D Bin Packing Reinforcement Learning Solution with Buffer

Puche, Aaron Valero; Lee, Sukhan

doi:10.1109/iros47612.2022.9982095

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…A buffer can be employed to allow for multi-item action selection, and an agnostic data augmentation strategy paired with a model-based RL method can lead to results that outperform the state-of-the-art solutions in space utilization [100]. This model was based on the popular algorithm AlphaGo, which is a great adaptation for tackling the mixed palletizing problem without the need for heavy computational resources.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Mixed Palletizing for Smart Warehouse Environments: Sustainability Review of Existing Methods

Daios,

Kladovasilakis,

Kostavelis

2024

Sustainability

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Mixed palletizing constitutes one of the problems in the logistics domain aroused from the need for fast product movement to satisfy the continuously increasing number of customers with the demand for highly personalized goods. In a demanding environment, such as warehouses, where break bulk and the consolidation of loads take up most of the working time, the automation of mixed palletizing can lead to increased efficiency and speed while keeping errors to a minimum. Space utilization of pallets enables savings in storage and transportation costs, boosting the overall sustainable role of the Supply Chain sector. This paper presents the proposed approaches to mixed palletizing stemming from different fields, with a focus on recent developments in the application of Industry 4.0 technologies. Our research highlights quite a few areas that require attention from researchers.

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Section: Reinforcement Learningmentioning

confidence: 99%

Mixed Palletizing for Smart Warehouse Environments: Sustainability Review of Existing Methods

Daios,

Kladovasilakis,

Kostavelis

2024

Sustainability

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“…Priority constraints also play a pivotal role in scenarios where specific items demand special handling due to factors like fragility, value, or time-sensitivity delivery. These constraints guide the packing strategy, ensuring that high-priority items are placed with care and precision [16,[21][22][23].…”

Section: Balancing Priority Fragility or Other Constraintsmentioning

confidence: 99%

“…Item rotation or orientation constraints play a crucial role in multidimensional binpacking problems, where items or boxes come in various shapes and sizes. These constraints pertain to how each box can be oriented or rotated within a container or bin; however, these can contribute to increased complexity [15,22,26,27]. Other constraints like dimension, weight, and volume are considered in most studies to address the unique requirements of different scenarios.…”

Section: Balancing Priority Fragility or Other Constraintsmentioning

confidence: 99%

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Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Lopes,

Trovati,

Pereira

2024

Future Internet

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Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach.

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“…Recently, Zhao et al generated a dataset with 64 instances addressing this issue [20,21]. A large body of research uses the guillotine cut method to generate the problem instances [22][23][24]. Since, in this method, the optimal solution for each problem instance is known, these datasets are widely used to test the packing efficiency of online algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms

Ribeiro

Ananno

2023

Processes

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Packing products into a pallet or other medium is an unavoidable activity for producing companies. In many cases, packing is based on operator experience and training using packing patterns that have worked before. Automated packing, on the other hand, requires a systematic procedure for devising packing solutions. In the scientific literature, this problem is known as 3D bin packing (3DBP) and many authors have proposed exact and heuristic solutions for many variations of the problem. There is, however, a lack of datasets that can be used to test and validate such solutions. Many of the available datasets use randomly generated products with extremely limited connection to real practice. Furthermore, they contain a reduced number of product configurations and ignore that packing relates to customers’ orders, which have specific relative mixes of products. This paper proposes a software toolbox for generating arbitrarily large datasets for 3DBPP based on real industry data. The toolbox was developed in connection with the analysis of a real dataset from the food and beverages sector, which enabled the creation of several synthetic datasets. The toolbox and the synthetic datasets are publicly available and can be used to generate additional data for testing and validating 3DBP solutions. The industry is increasingly becoming data dependent and driven. The ability to generate good quality synthetic data to support the development of solutions to real industry problems is of extreme importance. This work is a step in that direction in a domain where open data are scarce.

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Online 3D Bin Packing Reinforcement Learning Solution with Buffer

Cited by 10 publications

References 8 publications

Mixed Palletizing for Smart Warehouse Environments: Sustainability Review of Existing Methods

Mixed Palletizing for Smart Warehouse Environments: Sustainability Review of Existing Methods

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms

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