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Instance space analysis for the car sequencing problem

Author

Listed:
  • Yuan Sun

    (Monash University)

  • Samuel Esler

    (Monash University)

  • Dhananjay Thiruvady

    (Deakin University)

  • Andreas T. Ernst

    (Monash University)

  • Xiaodong Li

    (RMIT University)

  • Kerri Morgan

    (Deakin University)

Abstract

We 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.

Suggested Citation

  • Yuan Sun & Samuel Esler & Dhananjay Thiruvady & Andreas T. Ernst & Xiaodong Li & Kerri Morgan, 2024. "Instance space analysis for the car sequencing problem," Annals of Operations Research, Springer, vol. 341(1), pages 41-69, October.
    • Handle: RePEc:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-022-04860-8
    DOI: 10.1007/s10479-022-04860-8
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    References listed on IDEAS

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    1. Solnon, Christine & Cung, Van Dat & Nguyen, Alain & Artigues, Christian, 2008. "The car sequencing problem: Overview of state-of-the-art methods and industrial case-study of the ROADEF'2005 challenge problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 912-927, December.
    2. Uli Golle & Franz Rothlauf & Nils Boysen, 2015. "Iterative beam search for car sequencing," Annals of Operations Research, Springer, vol. 226(1), pages 239-254, March.
    3. Joaquín Bautista & Jordi Pereira & Belarmino Adenso-Díaz, 2008. "A Beam Search approach for the optimization version of the Car Sequencing Problem," Annals of Operations Research, Springer, vol. 159(1), pages 233-244, March.
    4. M Gravel & C Gagné & W L Price, 2005. "Review and comparison of three methods for the solution of the car sequencing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1287-1295, November.
    5. Dhananjay Thiruvady & Kerri Morgan & Amiza Amir & Andreas T. Ernst, 2020. "Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2696-2711, May.
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