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Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system

In the field of e-commerce warehousing, maximizing the utilization of packing bins is a fundamental goal for all major logistics enterprises. However, determining the appropriate size of packing bins poses a practical challenge for many logistics companies. Given the limited research on the open-siz...

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Autores principales: Yang, Jianglong, Liang, Kaibo, Liu, Huwei, Shan, Man, Zhou, Li, Kong, Lingjie, Li, Xiaolan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588690/
https://www.ncbi.nlm.nih.gov/pubmed/37869457
http://dx.doi.org/10.7717/peerj-cs.1613
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author Yang, Jianglong
Liang, Kaibo
Liu, Huwei
Shan, Man
Zhou, Li
Kong, Lingjie
Li, Xiaolan
author_facet Yang, Jianglong
Liang, Kaibo
Liu, Huwei
Shan, Man
Zhou, Li
Kong, Lingjie
Li, Xiaolan
author_sort Yang, Jianglong
collection PubMed
description In the field of e-commerce warehousing, maximizing the utilization of packing bins is a fundamental goal for all major logistics enterprises. However, determining the appropriate size of packing bins poses a practical challenge for many logistics companies. Given the limited research on the open-size 3D bin packing problem as well as the high complexity and lengthy computation time of existing models, this study focuses on optimizing multiple-bin sizes within the e-commerce context. Building upon existing research, we propose a hybrid integer programming model, denoted as the three dimensional multiple option dimensional rectangular packing problem (3D-MODRPP), to address the multiple-bin size 3D bin packing problem. Additionally, we leverage well-established hardware and software technologies to propose a 3D bin packing system capable of accommodating multiple bin types with open dimensions. To reduce the complexity of the model and the number of constraints, we introduce a novel assumption method for 0–1 integer variables in the overlap and rotation constraints. By applying this approach, we significantly streamline the computational complexity associated with the model calculations. Furthermore, we refine the dataset by developing a customized version based on the classical Three-Dimensional One-Size Dependent Rectangular Packing Problem (3D-ODRPP) dataset, leading to improved outcomes. Through comprehensive analysis of the research results, our model exhibits remarkable advancements in addressing the strong heterogeneous bin packing problem, the weak heterogeneous bin packing problem, the actual bin packing problem, and the bin packing problem with multiple bin types and open sizes. Specifically, it significantly reduces model complexity and computation time and increases space utilization. The system designed in this study paves the way for practical applications based on the proposed model, providing researchers with broader research prospects and directions to expand the scope of investigation in the field of 3D bin packing. Consequently, this system contributes to solving complex 3D packing problems, reducing space waste, and enhancing transportation efficiency.
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spelling pubmed-105886902023-10-21 Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system Yang, Jianglong Liang, Kaibo Liu, Huwei Shan, Man Zhou, Li Kong, Lingjie Li, Xiaolan PeerJ Comput Sci Adaptive and Self-Organizing Systems In the field of e-commerce warehousing, maximizing the utilization of packing bins is a fundamental goal for all major logistics enterprises. However, determining the appropriate size of packing bins poses a practical challenge for many logistics companies. Given the limited research on the open-size 3D bin packing problem as well as the high complexity and lengthy computation time of existing models, this study focuses on optimizing multiple-bin sizes within the e-commerce context. Building upon existing research, we propose a hybrid integer programming model, denoted as the three dimensional multiple option dimensional rectangular packing problem (3D-MODRPP), to address the multiple-bin size 3D bin packing problem. Additionally, we leverage well-established hardware and software technologies to propose a 3D bin packing system capable of accommodating multiple bin types with open dimensions. To reduce the complexity of the model and the number of constraints, we introduce a novel assumption method for 0–1 integer variables in the overlap and rotation constraints. By applying this approach, we significantly streamline the computational complexity associated with the model calculations. Furthermore, we refine the dataset by developing a customized version based on the classical Three-Dimensional One-Size Dependent Rectangular Packing Problem (3D-ODRPP) dataset, leading to improved outcomes. Through comprehensive analysis of the research results, our model exhibits remarkable advancements in addressing the strong heterogeneous bin packing problem, the weak heterogeneous bin packing problem, the actual bin packing problem, and the bin packing problem with multiple bin types and open sizes. Specifically, it significantly reduces model complexity and computation time and increases space utilization. The system designed in this study paves the way for practical applications based on the proposed model, providing researchers with broader research prospects and directions to expand the scope of investigation in the field of 3D bin packing. Consequently, this system contributes to solving complex 3D packing problems, reducing space waste, and enhancing transportation efficiency. PeerJ Inc. 2023-10-09 /pmc/articles/PMC10588690/ /pubmed/37869457 http://dx.doi.org/10.7717/peerj-cs.1613 Text en ©2023 Yang et al. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits using, remixing, and building upon the work non-commercially, as long as it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Adaptive and Self-Organizing Systems
Yang, Jianglong
Liang, Kaibo
Liu, Huwei
Shan, Man
Zhou, Li
Kong, Lingjie
Li, Xiaolan
Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title_full Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title_fullStr Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title_full_unstemmed Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title_short Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
title_sort optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system
topic Adaptive and Self-Organizing Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588690/
https://www.ncbi.nlm.nih.gov/pubmed/37869457
http://dx.doi.org/10.7717/peerj-cs.1613
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