IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i11p2418-d1153847.html
   My bibliography  Save this article

Kit of Uniformly Deployed Sets for p -Location Problems

Author

Listed:
  • Jaroslav Janáček

    (Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia)

  • Marek Kvet

    (Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia)

  • Peter Czimmermann

    (Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia)

Abstract

This paper deals with p -location problem solving processes based on a decomposition, which separates the creation of a uniformly deployed set of p -location problems from the solution of the p -location problem for that specific instance. The research presented in this paper is focused on methods of construction of uniformly deployed sets of solutions and the examination of their impact on the efficiency of subsequent optimization algorithms. The approaches to the construction are used for the constitution of predetermined families of uniformly deployed sets of p -location problem solutions, which have standard sizes. We introduce two methods of uniformly deployed set construction: the first one is based on composition, followed by an enlargement process; and the second one makes use of voltage graphs. The construction approaches are completed by an algorithm, which adjusts the set of solutions to the sizes of a solved instance. The influence of a set construction approach on solving process efficiency is studied on real-world benchmarks, which include both the p -median objective function and the generalized disutility function. The solving process is performed alternatively using the swap or path-relinking based methods. Results of the computational study obtained by all combinations of the mentioned approaches are presented and evaluated in the concluding part of the paper to make the studied characteristics visible.

Suggested Citation

  • Jaroslav Janáček & Marek Kvet & Peter Czimmermann, 2023. "Kit of Uniformly Deployed Sets for p -Location Problems," Mathematics, MDPI, vol. 11(11), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2418-:d:1153847
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/11/2418/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/11/2418/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brotcorne, Luce & Laporte, Gilbert & Semet, Frederic, 2003. "Ambulance location and relocation models," European Journal of Operational Research, Elsevier, vol. 147(3), pages 451-463, June.
    2. Armann Ingolfsson & Susan Budge & Erhan Erkut, 2008. "Optimal ambulance location with random delays and travel times," Health Care Management Science, Springer, vol. 11(3), pages 262-274, September.
    3. Karatas, Mumtaz & Yakıcı, Ertan, 2019. "An analysis of p-median location problem: Effects of backup service level and demand assignment policy," European Journal of Operational Research, Elsevier, vol. 272(1), pages 207-218.
    4. Sourour Elloumi & Martine Labbé & Yves Pochet, 2004. "A New Formulation and Resolution Method for the p-Center Problem," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 84-94, February.
    5. Sayah, David & Irnich, Stefan, 2017. "A new compact formulation for the discrete p-dispersion problem," European Journal of Operational Research, Elsevier, vol. 256(1), pages 62-67.
    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. Jaroslav Janáček & Marek Kvet, 2021. "Efficient incrementing heuristics for generalized p-location problems," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 989-1000, September.
    2. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    3. Rajagopalan, Hari K. & Saydam, Cem, 2009. "A minimum expected response model: Formulation, heuristic solution, and application," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 253-262, December.
    4. Soovin Yoon & Laura A. Albert, 2018. "An expected coverage model with a cutoff priority queue," Health Care Management Science, Springer, vol. 21(4), pages 517-533, December.
    5. Wajid, Shayesta & Nezamuddin, N., 2023. "Capturing delays in response of emergency services in Delhi," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    6. Zvi Drezner & Vladimir Marianov & George O. Wesolowsky, 2016. "Maximizing the minimum cover probability by emergency facilities," Annals of Operations Research, Springer, vol. 246(1), pages 349-362, November.
    7. Nilay Noyan, 2010. "Alternate risk measures for emergency medical service system design," Annals of Operations Research, Springer, vol. 181(1), pages 559-589, December.
    8. Westgate, Bradford S. & Woodard, Dawn B. & Matteson, David S. & Henderson, Shane G., 2016. "Large-network travel time distribution estimation for ambulances," European Journal of Operational Research, Elsevier, vol. 252(1), pages 322-333.
    9. G Erdoğan & E Erkut & A Ingolfsson & G Laporte, 2010. "Scheduling ambulance crews for maximum coverage," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 543-550, April.
    10. Jing Yao & Alan T. Murray, 2014. "Locational Effectiveness of Clinics Providing Sexual and Reproductive Health Services to Women in Rural Mozambique," International Regional Science Review, , vol. 37(2), pages 172-193, April.
    11. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    12. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    13. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    14. Ibrahim Çapar & Sharif H Melouk & Burcu B Keskin, 2017. "Alternative metrics to measure EMS system performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 792-808, July.
    15. Ľuboš Buzna & Peter Czimmermann, 2021. "On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia," Mathematics, MDPI, vol. 9(17), pages 1-30, September.
    16. Dirk Degel & Lara Wiesche & Sebastian Rachuba & Brigitte Werners, 2015. "Time-dependent ambulance allocation considering data-driven empirically required coverage," Health Care Management Science, Springer, vol. 18(4), pages 444-458, December.
    17. Susana Baptista & Rui Oliveira, 2012. "A case study on the application of an approximated hypercube model to emergency medical systems management," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 559-581, December.
    18. Kenneth C. Chong & Shane G. Henderson & Mark E. Lewis, 2016. "The Vehicle Mix Decision in Emergency Medical Service Systems," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 347-360, July.
    19. Thije van Barneveld, 2016. "The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 370-384, May.
    20. Soovin Yoon & Laura A. Albert & Veronica M. White, 2021. "A Stochastic Programming Approach for Locating and Dispatching Two Types of Ambulances," Transportation Science, INFORMS, vol. 55(2), pages 275-296, March.

    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:gam:jmathe:v:11:y:2023:i:11:p:2418-:d:1153847. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.