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A case study of algorithm selection for the traveling thief problem

Author

Listed:
  • Markus Wagner

    (The University of Adelaide)

  • Marius Lindauer

    (Albert-Ludwigs-Universität Freiburg)

  • Mustafa Mısır

    (Nanjing University of Aeronautics and Astronautics)

  • Samadhi Nallaperuma

    (University of Sheffield)

  • Frank Hutter

    (Albert-Ludwigs-Universität Freiburg)

Abstract

Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.

Suggested Citation

  • Markus Wagner & Marius Lindauer & Mustafa Mısır & Samadhi Nallaperuma & Frank Hutter, 2018. "A case study of algorithm selection for the traveling thief problem," Journal of Heuristics, Springer, vol. 24(3), pages 295-320, June.
    • Handle: RePEc:spr:joheur:v:24:y:2018:i:3:d:10.1007_s10732-017-9328-y
    DOI: 10.1007/s10732-017-9328-y
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    References listed on IDEAS

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    1. Gerhard Reinelt, 1991. "TSPLIB—A Traveling Salesman Problem Library," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 376-384, November.
    2. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    3. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    4. David Applegate & William Cook & André Rohe, 2003. "Chained Lin-Kernighan for Large Traveling Salesman Problems," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 82-92, February.
    5. Silvano Martello & David Pisinger & Paolo Toth, 1999. "Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem," Management Science, INFORMS, vol. 45(3), pages 414-424, March.
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    Cited by:

    1. Van Bulck, David & Goossens, Dries & Clarner, Jan-Patrick & Dimitsas, Angelos & Fonseca, George H.G. & Lamas-Fernandez, Carlos & Lester, Martin Mariusz & Pedersen, Jaap & Phillips, Antony E. & Rosati,, 2024. "Which algorithm to select in sports timetabling?," European Journal of Operational Research, Elsevier, vol. 318(2), pages 575-591.
    2. Jonatas B. C. Chagas & Julian Blank & Markus Wagner & Marcone J. F. Souza & Kalyanmoy Deb, 2021. "A non-dominated sorting based customized random-key genetic algorithm for the bi-objective traveling thief problem," Journal of Heuristics, Springer, vol. 27(3), pages 267-301, June.
    3. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.

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