•  Home
  • Library
  • DRO home
Submit research Contact DRO

DRO

Openly accessible

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, DhananjayORCID iD for Thiruvady, Dhananjay orcid.org/0000-0002-8011-933X
Ernst, AT
Li, X
Morgan, KerriORCID iD for 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
 
Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus Google Scholar Search Google Scholar
Access Statistics: 33 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Sat, 09 Jul 2022, 21:24:58 EST

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.