IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v26y2020i6d10.1007_s10732-020-09454-w.html
   My bibliography  Save this article

Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

Author

Listed:
  • Felipe Campelo

    (Aston University
    Universidade Federal de Minas Gerais)

  • Elizabeth F. Wanner

    (Aston University
    CEFET-MG)

Abstract

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm’s step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms.

Suggested Citation

  • Felipe Campelo & Elizabeth F. Wanner, 2020. "Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances," Journal of Heuristics, Springer, vol. 26(6), pages 851-883, December.
    • Handle: RePEc:spr:joheur:v:26:y:2020:i:6:d:10.1007_s10732-020-09454-w
    DOI: 10.1007/s10732-020-09454-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-020-09454-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10732-020-09454-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Catherine C. McGeoch, 1996. "Feature Article---Toward an Experimental Method for Algorithm Simulation," INFORMS Journal on Computing, INFORMS, vol. 8(1), pages 1-15, February.
    2. J. N. Hooker, 1994. "Needed: An Empirical Science of Algorithms," Operations Research, INFORMS, vol. 42(2), pages 201-212, April.
    3. Felipe Campelo & Fernanda Takahashi, 2019. "Sample size estimation for power and accuracy in the experimental comparison of algorithms," Journal of Heuristics, Springer, vol. 25(2), pages 305-338, April.
    4. Marie Coffin & Matthew J. Saltzman, 2000. "Statistical Analysis of Computational Tests of Algorithms and Heuristics," INFORMS Journal on Computing, INFORMS, vol. 12(1), pages 24-44, February.
    5. Vallada, Eva & Ruiz, Rubén, 2011. "A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 211(3), pages 612-622, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jie Zhang & Yifan Zhu & Tao Wang & Weiping Wang & Rui Wang & Xiaobo Li, 2022. "An Improved Intelligent Auction Mechanism for Emergency Material Delivery," Mathematics, MDPI, vol. 10(13), pages 1-30, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Felipe Campelo & Fernanda Takahashi, 2019. "Sample size estimation for power and accuracy in the experimental comparison of algorithms," Journal of Heuristics, Springer, vol. 25(2), pages 305-338, April.
    2. Nicholas G. Hall & Marc E. Posner, 2001. "Generating Experimental Data for Computational Testing with Machine Scheduling Applications," Operations Research, INFORMS, vol. 49(6), pages 854-865, December.
    3. Dung-Ying Lin & Tzu-Yun Huang, 2021. "A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    4. Yung-Chia Chang & Kuei-Hu Chang & Ching-Ping Zheng, 2022. "Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards," Mathematics, MDPI, vol. 10(13), pages 1-21, July.
    5. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    6. Jin Xu & Natarajan Gautam, 2020. "On competitive analysis for polling systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(6), pages 404-419, September.
    7. Jun-Ho Lee & Hyun-Jung Kim, 2021. "A heuristic algorithm for identical parallel machine scheduling: splitting jobs, sequence-dependent setup times, and limited setup operators," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 992-1026, December.
    8. Absalom E Ezugwu & Olawale J Adeleke & Serestina Viriri, 2018. "Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-23, July.
    9. Mesquita-Cunha, Mariana & Figueira, José Rui & Barbosa-Póvoa, Ana Paula, 2023. "New ϵ−constraint methods for multi-objective integer linear programming: A Pareto front representation approach," European Journal of Operational Research, Elsevier, vol. 306(1), pages 286-307.
    10. Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
    11. Mastrolilli, Monaldo & Bianchi, Leonora, 2005. "Core instances for testing: A case study," European Journal of Operational Research, Elsevier, vol. 166(1), pages 51-62, October.
    12. F. Angel-Bello & Y. Cardona-Valdés & A. Álvarez, 2019. "Mixed integer formulations for the multiple minimum latency problem," Operational Research, Springer, vol. 19(2), pages 369-398, June.
    13. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    14. Arnd Huchzermeier & Tobias Mönch, 2023. "Mixed‐model assembly lines with variable takt and open stations," Production and Operations Management, Production and Operations Management Society, vol. 32(3), pages 704-722, March.
    15. Ching-Jong Liao & Cheng-Hsiung Lee & Hsing-Tzu Tsai, 2016. "Scheduling with multi-attribute set-up times on unrelated parallel machines," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4839-4853, August.
    16. Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2018. "A new methodology for the open-pit mine production scheduling problem," Omega, Elsevier, vol. 81(C), pages 169-182.
    17. Pablo Moscato & Luke Mathieson & Mohammad Nazmul Haque, 2021. "Augmented intuition: a bridge between theory and practice," Journal of Heuristics, Springer, vol. 27(4), pages 497-547, August.
    18. Pohl, Maximilian & Kolisch, Rainer & Schiffer, Maximilian, 2021. "Runway scheduling during winter operations," Omega, Elsevier, vol. 102(C).
    19. A.C. Mahasinghe & L.A. Sarathchandra, 2020. "An Optimization Model for Production Planning in the Synthetic Fertilizer Industry," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 28-62, September.
    20. Guo, Z.X. & Ngai, E.W.T. & Yang, Can & Liang, Xuedong, 2015. "An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 16-28.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joheur:v:26:y:2020:i:6:d:10.1007_s10732-020-09454-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.