IDEAS home Printed from https://ideas.repec.org/a/spr/topjnl/v25y2017i3d10.1007_s11750-017-0461-4.html
   My bibliography  Save this article

On solving a large-scale problem on facility location and customer assignment with interaction costs along a time horizon

Author

Listed:
  • Laureano F. Escudero

    (Universidad Rey Juan Carlos)

  • Celeste Pizarro Romero

    (Universidad Rey Juan Carlos)

Abstract

A 0-1 quadratic programming model is presented for solving the strategic problem of timing the location of facilities and the assignment of customers to facilities in a multi-period setting. It is assumed that all parameters are known and, on the other hand, the quadratic character of the objective function is due to considering the interaction cost incurred by the joint assignment of customers belonging to different categories to a facility at a period. The plain use of a state-of-the-art MILP engine with capabilities for dealing with quadratic terms does not give any advantage over the matheuristic algorithm proposed in this work. In fact, the MILP engine was frequently running out of memory before reaching optimality for the equivalent mixed 0-1 linear formulation, being its best lower bound at that time instant too far from the incumbent solution for the large-sized instances which we have worked with. As an alternative, a fix-and-relax algorithm is presented. A deep computational comparison between MILP alternatives is performed, such that fix-and-relax provides a solution value very close to (and, frequently, a better than) the one provided by the MILP engine. The time required by fix-and-relax is very affordable, being frequently two times smaller than the time required by the MILP engine.

Suggested Citation

  • Laureano F. Escudero & Celeste Pizarro Romero, 2017. "On solving a large-scale problem on facility location and customer assignment with interaction costs along a time horizon," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 601-622, October.
  • Handle: RePEc:spr:topjnl:v:25:y:2017:i:3:d:10.1007_s11750-017-0461-4
    DOI: 10.1007/s11750-017-0461-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11750-017-0461-4
    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/s11750-017-0461-4?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. Sanjay Dominik Jena & Jean-François Cordeau & Bernard Gendron, 2015. "Dynamic Facility Location with Generalized Modular Capacities," Transportation Science, INFORMS, vol. 49(3), pages 484-499, August.
    2. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    3. Hanif Sherali, 2007. "RLT: A unified approach for discrete and continuous nonconvex optimization," Annals of Operations Research, Springer, vol. 149(1), pages 185-193, February.
    4. Stefan Nickel & Francisco Saldanha Gama, 2015. "Multi-Period Facility Location," Springer Books, in: Gilbert Laporte & Stefan Nickel & Francisco Saldanha da Gama (ed.), Location Science, edition 127, chapter 0, pages 289-310, Springer.
    5. Dillenberger, Christof & Escudero, Laureano F. & Wollensak, Artur & Zhang, Wu, 1994. "On practical resource allocation for production planning and scheduling with period overlapping setups," European Journal of Operational Research, Elsevier, vol. 75(2), pages 275-286, June.
    6. Maria Albareda-Sambola & Antonio Alonso-Ayuso & Laureano Escudero & Elena Fernández & Yolanda Hinojosa & Celeste Pizarro-Romero, 2010. "A computational comparison of several formulations for the multi-period incremental service facility location problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 62-80, July.
    7. Correia, Isabel & Melo, Teresa, 2016. "Multi-period capacitated facility location under delayed demand satisfaction," European Journal of Operational Research, Elsevier, vol. 255(3), pages 729-746.
    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. Aloullal, Afaf & Saldanha-da-Gama, Francisco & Todosijević, Raca, 2023. "Multi-period single-allocation hub location-routing: Models and heuristic solutions," European Journal of Operational Research, Elsevier, vol. 310(1), pages 53-70.
    2. Laureano F. Escudero & Juan F. Monge, 2021. "On Multistage Multiscale Stochastic Capacitated Multiple Allocation Hub Network Expansion Planning," Mathematics, MDPI, vol. 9(24), pages 1-39, December.

    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. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    2. Correia, Isabel & Melo, Teresa, 2016. "A computational comparison of formulations for a multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment," Technical Reports on Logistics of the Saarland Business School 11, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    3. Laureano F. Escudero & María Araceli Garín & Celeste Pizarro & Aitziber Unzueta, 2018. "On efficient matheuristic algorithms for multi-period stochastic facility location-assignment problems," Computational Optimization and Applications, Springer, vol. 70(3), pages 865-888, July.
    4. Han, Jialin & Zhang, Jiaxiang & Zeng, Bing & Mao, Mingsong, 2021. "Optimizing dynamic facility location-allocation for agricultural machinery maintenance using Benders decomposition," Omega, Elsevier, vol. 105(C).
    5. Silva, Allyson & Aloise, Daniel & Coelho, Leandro C. & Rocha, Caroline, 2021. "Heuristics for the dynamic facility location problem with modular capacities," European Journal of Operational Research, Elsevier, vol. 290(2), pages 435-452.
    6. Becker, Tristan & Lier, Stefan & Werners, Brigitte, 2019. "Value of modular production concepts in future chemical industry production networks," European Journal of Operational Research, Elsevier, vol. 276(3), pages 957-970.
    7. Yokoyama, Ryohei & Kitano, Hiroyuki & Wakui, Tetsuya, 2017. "Optimal operation of heat supply systems with piping network," Energy, Elsevier, vol. 137(C), pages 888-897.
    8. Tian, Xueyu & You, Fengqi, 2019. "Carbon-neutral hybrid energy systems with deep water source cooling, biomass heating, and geothermal heat and power," Applied Energy, Elsevier, vol. 250(C), pages 413-432.
    9. Sauvey, Christophe & Melo, Teresa & Correia, Isabel, 2019. "Two-phase heuristics for a multi-period capacitated facility location problem with service-differentiated customers," Technical Reports on Logistics of the Saarland Business School 16, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    10. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    11. Rostami, Borzou & Chassein, André & Hopf, Michael & Frey, Davide & Buchheim, Christoph & Malucelli, Federico & Goerigk, Marc, 2018. "The quadratic shortest path problem: complexity, approximability, and solution methods," European Journal of Operational Research, Elsevier, vol. 268(2), pages 473-485.
    12. Unai Aldasoro & María Merino & Gloria Pérez, 2019. "Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic," Annals of Operations Research, Springer, vol. 280(1), pages 151-187, September.
    13. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    14. Gupta, Renu & Bandopadhyaya, Lakshmisree & Puri, M. C., 1996. "Ranking in quadratic integer programming problems," European Journal of Operational Research, Elsevier, vol. 95(1), pages 231-236, November.
    15. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    16. Osman, Hany & Demirli, Kudret, 2010. "A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection," International Journal of Production Economics, Elsevier, vol. 124(1), pages 97-105, March.
    17. Correia, Isabel & Melo, Teresa, 2019. "Dynamic facility location problem with modular capacity adjustments under uncertainty," Technical Reports on Logistics of the Saarland Business School 17, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    18. Verbiest, Floor & Cornelissens, Trijntje & Springael, Johan, 2019. "A matheuristic approach for the design of multiproduct batch plants with parallel production lines," European Journal of Operational Research, Elsevier, vol. 273(3), pages 933-947.
    19. Fabio Furini & Emiliano Traversi, 2019. "Theoretical and computational study of several linearisation techniques for binary quadratic problems," Annals of Operations Research, Springer, vol. 279(1), pages 387-411, August.
    20. Biswas, Debajyoti & Alfandari, Laurent, 2022. "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1372-1391.

    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:topjnl:v:25:y:2017:i:3:d:10.1007_s11750-017-0461-4. 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.