Mommessin, C., Erlebach, T. and Shakhlevich, N. V. orcid.org/0000-0002-5225-4008 (2025) Classification and evaluation of the algorithms for vector bin packing. Computers & Operations Research, 173. 106860. ISSN 0305-0548
Abstract
Heuristics for Vector Bin Packing (VBP) play an important role in modern distributed computing systems and other applications aimed at optimizing the usage of multidimensional resources. In this paper we perform a systematic classification of heuristics for VBP, with the focus on construction heuristics. We bring together existing VBP algorithms and their tuning parameters, and propose new algorithms and new tuning parameters. For a less studied class of multi-bin algorithms, we explore their properties analytically, considering monotonic and anomalous behavior and approximation guarantees. For empirical evaluation, all algorithms are implemented as the Vectorpack library and assessed through extensive experiments. Our findings may serve as the basis for the development of more complex, hybrid algorithms, hyperheuristics and machine learning algorithms. The Vectorpack library can also be adjusted for addressing enhanced VBP problems with additional features, which arise in applications, especially those typical for modern distributed computing systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Vector bin packing, Bin packing, Heuristics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Algorithms & Complexity |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/T01461X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Oct 2024 08:26 |
Last Modified: | 11 Dec 2024 15:42 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cor.2024.106860 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218204 |