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A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-0248-8180
Linköping University, Department of Management and Engineering, Product Realisation. Linköping University, Faculty of Science & Engineering.
2023 (English)In: Processes, ISSN 2227-9717, Vol. 11, no 7, article id 1909Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2023. Vol. 11, no 7, article id 1909
Keywords [en]
3DBPP; toolbox; dataset; problem instance; online 3DBPP; offline 3DBPP
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-196731DOI: 10.3390/pr11071909ISI: 001038814300001OAI: oai:DiVA.org:liu-196731DiVA, id: diva2:1790061
Note

Funding Agencies|Strategic Innovation Program Produktion2030; Vinnova; Formas; Swedish Energy Agency [2021-01283]

Available from: 2023-08-22 Created: 2023-08-22 Last updated: 2024-01-08

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Ribeiro, LuisAnanno, Anan Ashrabi
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