Instance space analysis for the car sequencing problem Sun, Y, Esler, S, Thiruvady, Dhananjay, Ernst, AT, Li, X and Morgan, Kerri, Instance space analysis for the car sequencing problem, Annals of Operations Research, doi: 10.1007/s10479-022-04860-8. Attached Files Name Description MIMEType Size Downloads Title Instance space analysis for the car sequencing problem Author(s) Sun, Y Esler, S Thiruvady, Dhananjay orcid.org/0000-0002-8011-933X Ernst, AT Li, X Morgan, Kerri orcid.org/0000-0002-7263-8340 Journal name Annals of Operations Research Publisher Springer Science and Business Media LLC ISSN 0254-5330 1572-9338 Keyword(s) Algorithim selection Car sequencing Cominatorial optimisation Instance space analysis Machine learning Summary AbstractWe investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve? To do so, we carry out an instance space analysis for the car sequencing problem, by extracting a vector of problem features to characterize an instance. In order to visualize the instance space, the feature vectors are projected onto a 2-D space using dimensionality reduction techniques. The resulting 2-D visualizations provide new insights into the characteristics of the instances used for testing and how these characteristics influence the behaviours of an optimization algorithm. This analysis guides us in constructing a new set of benchmark instances with a range of instance properties. We demonstrate that these new instances are more diverse than the previous benchmarks, including some instances that are significantly more difficult to solve. We introduce two new algorithms for solving the car sequencing problem and compare them with four existing methods from the literature. Our new algorithms are shown to perform competitively for this problem but no single algorithm can outperform all others over all instances. This observation motivates us to build an algorithm selection model based on machine learning, to identify the niche in the instance space that an algorithm is expected to perform well on. Our analysis helps to understand problem hardness and select an appropriate algorithm for solving a given car sequencing problem instance. Language en DOI 10.1007/s10479-022-04860-8 Field of Research 01 Mathematical Sciences 08 Information and Computing Sciences 15 Commerce, Management, Tourism and Services HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30174252 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Sat, 09 Jul 2022, 21:24:58 EST Tue, 12 Jul 2022, 08:29:39 EST Tue, 12 Jul 2022, 08:42:04 EST Tue, 12 Jul 2022, 09:54:55 EST Wed, 13 Jul 2022, 06:26:55 EST Tue, 19 Jul 2022, 13:35:38 EST Tue, 19 Jul 2022, 13:39:38 EST Tue, 19 Jul 2022, 15:08:09 EST Sat, 30 Jul 2022, 08:35:09 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 33 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Sat, 09 Jul 2022, 21:24:58 EST