IDEAS home Printed from https://ideas.repec.org/p/ehu/biltok/5572.html
   My bibliography  Save this paper

A note on the implementation of the BFC-MSMIP algorithm in C++ by using COIN-OR as an optimization engine

Author

Listed:
  • Escudero Bueno, Laureano F.
  • Garín Martín, María Araceli
  • Merino Maestre, María
  • Pérez Sainz de Rozas, Gloria

Abstract

The aim of this technical report is to present some detailed explanations in order to help to understand and use the algorithm Branch and Fix Coordination for solving MultiStage Mixed Integer Problems (BFC- MSMIP). We have developed an algorithmic approach implemented in a C++ experimental code that uses the optimization engine COmputational INfrastructure for Operations Research (COIN-OR) for solving the auxiliary linear and mixed 0-1 submodels. Now, we give the computational and implementational descrip- tion in order to use this open optimization software not only in the implementation of our procedure but also in similar schemes to be developed by the users.

Suggested Citation

  • Escudero Bueno, Laureano F. & Garín Martín, María Araceli & Merino Maestre, María & Pérez Sainz de Rozas, Gloria, 2010. "A note on the implementation of the BFC-MSMIP algorithm in C++ by using COIN-OR as an optimization engine," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    • Handle: RePEc:ehu:biltok:5572
    as

    Download full text from publisher

    File URL: https://addi.ehu.es/handle/10810/5572
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laureano Escudero & Araceli Garín & María Merino & Gloria Pérez, 2009. "BFC-MSMIP: an exact branch-and-fix coordination approach for solving multistage stochastic mixed 0–1 problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 96-122, July.
    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. Giovanni Pantuso & Trine K. Boomsma, 2020. "On the number of stages in multistage stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 581-603, September.
    2. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
    3. Semih Atakan & Suvrajeet Sen, 2018. "A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs," Computational Management Science, Springer, vol. 15(3), pages 501-540, October.
    4. 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.
    5. Laureano Escudero, 2009. "On a mixture of the fix-and-relax coordination and Lagrangian substitution schemes for multistage stochastic mixed integer programming," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 5-29, July.
    6. Escudero Bueno, Laureano F. & Garín Martín, María Araceli & Merino Maestre, María & Pérez Sainz de Rozas, Gloria, 2011. "A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    7. Pagès-Bernaus, Adela & Pérez-Valdés, Gerardo & Tomasgard, Asgeir, 2015. "A parallelised distributed implementation of a Branch and Fix Coordination algorithm," European Journal of Operational Research, Elsevier, vol. 244(1), pages 77-85.
    8. Giovanni Pantuso & Kjetil Fagerholt & Stein W. Wallace, 2015. "Solving Hierarchical Stochastic Programs: Application to the Maritime Fleet Renewal Problem," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 89-102, February.
    9. Escudero, Laureano F. & Landete, Mercedes & Rodríguez-Chía, Antonio M., 2011. "Stochastic set packing problem," European Journal of Operational Research, Elsevier, vol. 211(2), pages 232-240, June.
    10. Fernando Veliz & Jean-Paul Watson & Andres Weintraub & Roger Wets & David Woodruff, 2015. "Stochastic optimization models in forest planning: a progressive hedging solution approach," Annals of Operations Research, Springer, vol. 232(1), pages 259-274, September.

    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:ehu:biltok:5572. 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: Alcira Macías (email available below). General contact details of provider: https://edirc.repec.org/data/deehues.html .

    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.