IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v242y2016i1d10.1007_s10479-014-1686-3.html
   My bibliography  Save this article

An efficient local search with noising strategy for Google Machine Reassignment problem

Author

Listed:
  • Haris Gavranović

    (International University of Sarajevo)

  • Mirsad Buljubašić

    (Ecole des Mines d’Ales)

Abstract

We present a local search method combined with noising strategy to efficiently solve the Google Machine Reassignment problem (GMRP), proposed at the ROADEF/EURO Challenge 2012 competition. The GMRP is a challenging and novel optimization problem, aimed at maximizing the usage of a set of machines by reallocating processes among those machines in a cost-efficient manner, while respecting a set of technological constraints. The search explores three different neighborhoods. Intensification and diversification of the search is achieved through the noising strategy, sorting the processes and search restarts. The noising is done by simple and suitable change of the objective function. The method is tested on 30 instances proposed by Google and used challenge evaluation. Most of the numerical results obtained here are proven to be optimal, near optimal, or the best known. The presented method was ranked first at ROADEF/EURO Challenge 2012 competition.

Suggested Citation

  • Haris Gavranović & Mirsad Buljubašić, 2016. "An efficient local search with noising strategy for Google Machine Reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 19-31, July.
  • Handle: RePEc:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1686-3
    DOI: 10.1007/s10479-014-1686-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-014-1686-3
    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/s10479-014-1686-3?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. Mutsunori Yagiura & Toshihide Ibaraki & Fred Glover, 2004. "An Ejection Chain Approach for the Generalized Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 133-151, May.
    2. Charon, Irene & Hudry, Olivier, 2001. "The noising methods: A generalization of some metaheuristics," European Journal of Operational Research, Elsevier, vol. 135(1), pages 86-101, November.
    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. Michel Vasquez & Mirsad Buljubasic & Saïd Hanafi, 2023. "An efficient scenario penalization matheuristic for a stochastic scheduling problem," Journal of Heuristics, Springer, vol. 29(2), pages 383-408, 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. M Büther, 2010. "Reducing the elastic generalized assignment problem to the standard generalized assignment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1582-1595, November.
    2. Büther, Marcel, 2007. "Reducing the elastic generalized assignment problem to the standard generalized assignment problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 632, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    3. Monfared, M.A.S. & Etemadi, M., 2006. "The impact of energy function structure on solving generalized assignment problem using Hopfield neural network," European Journal of Operational Research, Elsevier, vol. 168(2), pages 645-654, January.
    4. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    5. Büther, Marcel, 2008. "Beam search for the elastic generalized assignment problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 634, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    6. Mehdi Mrad & Anis Gharbi & Mohamed Haouari & Mohamed Kharbeche, 2016. "An optimization-based heuristic for the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 115-132, July.
    7. Olivier Hudry & Bernard Monjardet, 2010. "Consensus theories: An oriented survey," Documents de travail du Centre d'Economie de la Sorbonne 10057, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Woodcock, Andrew J. & Wilson, John M., 2010. "A hybrid tabu search/branch & bound approach to solving the generalized assignment problem," European Journal of Operational Research, Elsevier, vol. 207(2), pages 566-578, December.
    9. Gelareh, Shahin & Glover, Fred & Guemri, Oualid & Hanafi, Saïd & Nduwayo, Placide & Todosijević, Raca, 2020. "A comparative study of formulations for a cross-dock door assignment problem," Omega, Elsevier, vol. 91(C).
    10. Yi Zhou & Jin-Kao Hao & Adrien Goëffon, 2016. "A three-phased local search approach for the clique partitioning problem," Journal of Combinatorial Optimization, Springer, vol. 32(2), pages 469-491, August.
    11. Drexl, Andreas & Jørnsten, Kurt, 2007. "Pricing the generalized assignment problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 627, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    12. Yagiura, Mutsunori & Ibaraki, Toshihide & Glover, Fred, 2006. "A path relinking approach with ejection chains for the generalized assignment problem," European Journal of Operational Research, Elsevier, vol. 169(2), pages 548-569, March.
    13. M. Gaudioso & L. Moccia & M. F. Monaco, 2010. "Repulsive Assignment Problem," Journal of Optimization Theory and Applications, Springer, vol. 144(2), pages 255-273, February.
    14. Franck Butelle & Laurent Alfandari & Camille Coti & Lucian Finta & Lucas Létocart & Gérard Plateau & Frédéric Roupin & Antoine Rozenknop & Roberto Wolfler Calvo, 2016. "Fast machine reassignment," Annals of Operations Research, Springer, vol. 242(1), pages 133-160, July.
    15. Kress, Dominik & Meiswinkel, Sebastian & Pesch, Erwin, 2019. "Straddle carrier routing at seaport container terminals in the presence of short term quay crane buffer areas," European Journal of Operational Research, Elsevier, vol. 279(3), pages 732-750.
    16. Salim Haddadi, 2019. "Variable-fixing then subgradient optimization guided very large scale neighborhood search for the generalized assignment problem," 4OR, Springer, vol. 17(3), pages 261-295, September.
    17. Irène Charon & Olivier Hudry, 2010. "An updated survey on the linear ordering problem for weighted or unweighted tournaments," Annals of Operations Research, Springer, vol. 175(1), pages 107-158, March.
    18. D. Parr & J. Thompson, 2007. "Solving the multi-objective nurse scheduling problem with a weighted cost function," Annals of Operations Research, Springer, vol. 155(1), pages 279-288, November.
    19. Jacobson, Sheldon H. & McLay, Laura A., 2009. "Applying statistical tests to empirically compare tabu search parameters for MAX 3-SATISFIABILITY: A case study," Omega, Elsevier, vol. 37(3), pages 522-534, June.
    20. Sujeet Kumar Singh & Deepika Rani, 2019. "A branching algorithm to solve binary problem in uncertain environment: an application in machine allocation problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 1007-1023, 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:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1686-3. 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.