IDEAS home Printed from https://ideas.repec.org/a/spr/mathme/v100y2024i1d10.1007_s00186-023-00826-z.html
   My bibliography  Save this article

A test instance generator for multiobjective mixed-integer optimization

Author

Listed:
  • Gabriele Eichfelder

    (Technische Universität Ilmenau)

  • Tobias Gerlach

    (Technische Universität Ilmenau)

  • Leo Warnow

    (Technische Universität Ilmenau)

Abstract

Application problems can often not be solved adequately by numerical algorithms as several difficulties might arise at the same time. When developing and improving algorithms which hopefully allow to handle those difficulties in the future, good test instances are required. These can then be used to detect the strengths and weaknesses of different algorithmic approaches. In this paper we present a generator for test instances to evaluate solvers for multiobjective mixed-integer linear and nonlinear optimization problems. Based on test instances for purely continuous and purely integer problems with known efficient solutions and known nondominated points, suitable multiobjective mixed-integer test instances can be generated. The special structure allows to construct instances scalable in the number of variables and objective functions. Moreover, it allows to control the resulting efficient and nondominated sets as well as the number of efficient integer assignments.

Suggested Citation

  • Gabriele Eichfelder & Tobias Gerlach & Leo Warnow, 2024. "A test instance generator for multiobjective mixed-integer optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 100(1), pages 385-410, August.
  • Handle: RePEc:spr:mathme:v:100:y:2024:i:1:d:10.1007_s00186-023-00826-z
    DOI: 10.1007/s00186-023-00826-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00186-023-00826-z
    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/s00186-023-00826-z?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. De Santis, Marianna & Grani, Giorgio & Palagi, Laura, 2020. "Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming," European Journal of Operational Research, Elsevier, vol. 283(1), pages 57-69.
    2. Eichfelder, Gabriele & Warnow, Leo, 2023. "Advancements in the computation of enclosures for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 310(1), pages 315-327.
    3. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Integer Programming: The Balanced Box Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 735-754, November.
    4. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Mixed Integer Programming: The Triangle Splitting Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 597-618, November.
    Full references (including those not matched with items on IDEAS)

    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. Przybylski, Anthony & Gandibleux, Xavier, 2017. "Multi-objective branch and bound," European Journal of Operational Research, Elsevier, vol. 260(3), pages 856-872.
    2. Konur, Dinçer & Campbell, James F. & Monfared, Sepideh A., 2017. "Economic and environmental considerations in a stochastic inventory control model with order splitting under different delivery schedules among suppliers," Omega, Elsevier, vol. 71(C), pages 46-65.
    3. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    4. Hadi Charkhgard & Martin Savelsbergh & Masoud Talebian, 2018. "Nondominated Nash points: application of biobjective mixed integer programming," 4OR, Springer, vol. 16(2), pages 151-171, June.
    5. Luca Benvenuti & Alberto Santis & Marianna Santis & Daniele Patria, 2024. "Designing sustainable diet plans by solving triobjective integer programs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 100(3), pages 703-721, December.
    6. Boland, Natashia & Charkhgard, Hadi & Savelsbergh, Martin, 2017. "The Quadrant Shrinking Method: A simple and efficient algorithm for solving tri-objective integer programs," European Journal of Operational Research, Elsevier, vol. 260(3), pages 873-885.
    7. Atashpaz Gargari, Masoud & Sahraeian, Rashed, 2023. "An exact criterion space search method for a bi-objective nursing home location and allocation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 166-180.
    8. Cacchiani, Valentina & D’Ambrosio, Claudia, 2017. "A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs," European Journal of Operational Research, Elsevier, vol. 260(3), pages 920-933.
    9. Yıldız, Gazi Bilal & Soylu, Banu, 2019. "A multiobjective post-sales guarantee and repair services network design problem," International Journal of Production Economics, Elsevier, vol. 216(C), pages 305-320.
    10. Fattahi, Ali & Turkay, Metin, 2018. "A one direction search method to find the exact nondominated frontier of biobjective mixed-binary linear programming problems," European Journal of Operational Research, Elsevier, vol. 266(2), pages 415-425.
    11. Masar Al-Rabeeah & Santosh Kumar & Ali Al-Hasani & Elias Munapo & Andrew Eberhard, 2019. "Bi-objective integer programming analysis based on the characteristic equation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 937-944, October.
    12. Soylu, Banu & Katip, Hatice, 2019. "A multiobjective hub-airport location problem for an airline network design," European Journal of Operational Research, Elsevier, vol. 277(2), pages 412-425.
    13. De Santis, Marianna & Grani, Giorgio & Palagi, Laura, 2020. "Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming," European Journal of Operational Research, Elsevier, vol. 283(1), pages 57-69.
    14. Seyyed Amir Babak Rasmi & Ali Fattahi & Metin Türkay, 2021. "SASS: slicing with adaptive steps search method for finding the non-dominated points of tri-objective mixed-integer linear programming problems," Annals of Operations Research, Springer, vol. 296(1), pages 841-876, January.
    15. Karsu, Özlem & Kocaman, Ayse Selin, 2021. "Towards the Sustainable Development Goals: A Bi-objective framework for electricity access," Energy, Elsevier, vol. 216(C).
    16. Hongming Li & Xintao Li, 2022. "A Branch-and-Bound Algorithm for the Bi-Objective Quay Crane Scheduling Problem Based on Efficiency and Energy," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    17. Guillermo Cabrera-Guerrero & Matthias Ehrgott & Andrew J. Mason & Andrea Raith, 2022. "Bi-objective optimisation over a set of convex sub-problems," Annals of Operations Research, Springer, vol. 319(2), pages 1507-1532, December.
    18. Jesús Sáez-Aguado & Paula Camelia Trandafir, 2018. "Variants of the $$ \varepsilon $$ ε -constraint method for biobjective integer programming problems: application to p-median-cover problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(2), pages 251-283, April.
    19. Esmaeili, Somayeh & Bashiri, Mahdi & Amiri, Amirhossein, 2023. "An exact criterion space search algorithm for a bi-objective blood collection problem," European Journal of Operational Research, Elsevier, vol. 311(1), pages 210-232.
    20. Melendez, Kevin A. & Subramanian, Vignesh & Das, Tapas K. & Kwon, Changhyun, 2019. "Empowering end-use consumers of electricity to aggregate for demand-side participation," Applied Energy, Elsevier, vol. 248(C), pages 372-382.

    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:mathme:v:100:y:2024:i:1:d:10.1007_s00186-023-00826-z. 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.