Framework of algorithm portfolios for strip packing problem
In this paper selection of fast algorithm portfolios for 2SP packing problem is considered. The 2SP problem consists in placing rectangles on a strip of the given width for minimum strip length. The 2SP packing has application in many industries, but suitability of the related algorithms is limited...
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| Vydáno v: | Computers & industrial engineering Ročník 172; číslo part A; s. 108538 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2022
Elsevier |
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | In this paper selection of fast algorithm portfolios for 2SP packing problem is considered. The 2SP problem consists in placing rectangles on a strip of the given width for minimum strip length. The 2SP packing has application in many industries, but suitability of the related algorithms is limited by their runtimes. While solving combinatorial optimization problems, longer runtimes increase chances of obtaining higher quality solutions. This means that runtime vs solution quality trade-off is important in solving problems such as strip packing. Given some limited runtime, a method is needed to provide the best solution possible. However, a single algorithm outperforming all other methods under all possible conditions usually does not exist. Therefore, algorithm portfolios can reliably provide high quality solutions in the limited runtime. We propose a method choosing algorithm portfolios on the basis of the algorithm performance on a set of training instances. A portfolio covers the instances with the best solutions which could be obtained in the given runtime, subject to the minimum computational cost of the selected algorithms. The portfolios are evaluated in extensive experiments carried out on designed and literature datasets. We demonstrate that our method is capable of carrying over solution quality from the training datasets to the testing datasets. In other words, our algorithm selection method can learn from the training instances. We also compare performance of our portfolio selection method with some other more straightforward approaches to the portfolio selection.
•Algorithm selection problem for 2D strip packing considered.•A method constructing computational cost-optimum portfolios is proposed.•A trade-off between solution quality and runtime respected.•Algorithm portfolios are competitive with the state-of-the-art methods.•Mismatching of training and application dataset sizes and runtime limits are tested. |
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| AbstractList | In this paper selection of fast algorithm portfolios for 2SP packing problem is considered. The 2SP problem consists in placing rectangles on a strip of the given width for minimum strip length. The 2SP packing has application in many industries, but suitability of the related algorithms is limited by their runtimes. While solving combinatorial optimization problems, longer runtimes increase chances of obtaining higher quality solutions. This means that runtime vs solution quality trade-off is important in solving problems such as strip packing. Given some limited runtime, a method is needed to provide the best solution possible. However, a single algorithm outperforming all other methods under all possible conditions usually does not exist. Therefore, algorithm portfolios can reliably provide high quality solutions in the limited runtime. We propose a method choosing algorithm portfolios on the basis of the algorithm performance on a set of training instances. A portfolio covers the instances with the best solutions which could be obtained in the given runtime, subject to the minimum computational cost of the selected algorithms. The portfolios are evaluated in extensive experiments carried out on designed and literature datasets. We demonstrate that our method is capable of carrying over solution quality from the training datasets to the testing datasets. In other words, our algorithm selection method can learn from the training instances. We also compare performance of our portfolio selection method with some other more straightforward approaches to the portfolio selection. In this paper selection of fast algorithm portfolios for 2SP packing problem is considered. The 2SP problem consists in placing rectangles on a strip of the given width for minimum strip length. The 2SP packing has application in many industries, but suitability of the related algorithms is limited by their runtimes. While solving combinatorial optimization problems, longer runtimes increase chances of obtaining higher quality solutions. This means that runtime vs solution quality trade-off is important in solving problems such as strip packing. Given some limited runtime, a method is needed to provide the best solution possible. However, a single algorithm outperforming all other methods under all possible conditions usually does not exist. Therefore, algorithm portfolios can reliably provide high quality solutions in the limited runtime. We propose a method choosing algorithm portfolios on the basis of the algorithm performance on a set of training instances. A portfolio covers the instances with the best solutions which could be obtained in the given runtime, subject to the minimum computational cost of the selected algorithms. The portfolios are evaluated in extensive experiments carried out on designed and literature datasets. We demonstrate that our method is capable of carrying over solution quality from the training datasets to the testing datasets. In other words, our algorithm selection method can learn from the training instances. We also compare performance of our portfolio selection method with some other more straightforward approaches to the portfolio selection. •Algorithm selection problem for 2D strip packing considered.•A method constructing computational cost-optimum portfolios is proposed.•A trade-off between solution quality and runtime respected.•Algorithm portfolios are competitive with the state-of-the-art methods.•Mismatching of training and application dataset sizes and runtime limits are tested. |
| ArticleNumber | 108538 |
| Author | Drozdowski, Maciej Piechowiak, Kamil Sanlaville, Éric |
| Author_xml | – sequence: 1 givenname: Kamil surname: Piechowiak fullname: Piechowiak, Kamil email: Kamil.L.Piechowiak@student.put.poznan.pl organization: Institute of Computing Science, Poznań University of Technology, Poznań, Poland – sequence: 2 givenname: Maciej surname: Drozdowski fullname: Drozdowski, Maciej email: Maciej.Drozdowski@cs.put.poznan.pl organization: Institute of Computing Science, Poznań University of Technology, Poznań, Poland – sequence: 3 givenname: Éric orcidid: 0000-0001-9482-3945 surname: Sanlaville fullname: Sanlaville, Éric email: Eric.Sanlaville@univ-lehavre.fr organization: LITIS, Normandy University, UNIHAVRE, Le Havre, France |
| BackLink | https://normandie-univ.hal.science/hal-04218290$$DView record in HAL |
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| Issue | part A |
| Keywords | Algorithm portfolios 2D packing Runtime-quality trade-off Heuristics Algorithm selection problem algorithm portfolios algorithm selection problem runtime-quality trade-off |
| Language | English |
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| SubjectTerms | 2D packing Algorithm portfolios Algorithm selection problem Computer Science Heuristics Mathematics Runtime-quality trade-off |
| Title | Framework of algorithm portfolios for strip packing problem |
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