IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v242y2016i1d10.1007_s10479-014-1771-7.html
   My bibliography  Save this article

Simulated annealing for the machine reassignment problem

Author

Listed:
  • Gabriel M. Portal

    (Google Inc.)

  • Marcus Ritt

    (Universidade Federal do Rio Grande do Sul)

  • Leonardo M. Borba

    (Universidade Federal do Rio Grande do Sul)

  • Luciana S. Buriol

    (Universidade Federal do Rio Grande do Sul)

Abstract

Given an initial assignment of processes to machines, the machine reassignment problem is to find an assignment that improves the machine usage, subject to several resource and allocation constraints, and considering reassignment costs. We propose a heuristic based on simulated annealing for solving this problem. It uses two neighborhoods, one that moves a process from one machine to another, and a second one that swaps two processes on different machines. We present data structures that permit to validate and execute a move in time $$O(r+d)$$ O ( r + d ) where $$r$$ r is the number of resources and $$d$$ d the number of dependencies of the service the process belongs to. The heuristic runs with two different sets of parameters in parallel until a convergence criterion is satisfied. The machine reassignment problem was subject of the ROADEF/EURO challenge in 2012, and the proposed algorithm ranked fourth in the final round of the senior category of the competition.

Suggested Citation

  • Gabriel M. Portal & Marcus Ritt & Leonardo M. Borba & Luciana S. Buriol, 2016. "Simulated annealing for the machine reassignment problem," Annals of Operations Research, Springer, vol. 242(1), pages 93-114, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:1:d:10.1007_s10479-014-1771-7
    DOI: 10.1007/s10479-014-1771-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-014-1771-7
    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/s10479-014-1771-7?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. David S. Johnson & Cecilia R. Aragon & Lyle A. McGeoch & Catherine Schevon, 1989. "Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning," Operations Research, INFORMS, vol. 37(6), pages 865-892, December.
    2. Bruce Hajek, 1988. "Cooling Schedules for Optimal Annealing," Mathematics of Operations Research, INFORMS, vol. 13(2), pages 311-329, May.
    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. Fu Yan & Jianzhong Xu & Kumchol Yun, 2019. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information," Complexity, Hindawi, vol. 2019, pages 1-36, 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. Pirlot, Marc, 1996. "General local search methods," European Journal of Operational Research, Elsevier, vol. 92(3), pages 493-511, August.
    2. Van Buer, Michael G. & Woodruff, David L. & Olson, Rick T., 1999. "Solving the medium newspaper production/distribution problem," European Journal of Operational Research, Elsevier, vol. 115(2), pages 237-253, June.
    3. Yiyo Kuo, 2014. "Design method using hybrid of line-type and circular-type routes for transit network system optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 600-613, July.
    4. Doole, Graeme J., 2007. "A primer on implementing compressed simulated annealing for the optimisation of a constrained simulation model in Microsoft Excel," Working Papers 7420, University of Western Australia, School of Agricultural and Resource Economics.
    5. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    6. LeBlanc, Larry J. & Shtub, Avraham & Anandalingam, G., 1999. "Formulating and solving production planning problems," European Journal of Operational Research, Elsevier, vol. 112(1), pages 54-80, January.
    7. Marc Robini & Pierre-Jean Reissman, 2013. "From simulated annealing to stochastic continuation: a new trend in combinatorial optimization," Journal of Global Optimization, Springer, vol. 56(1), pages 185-215, May.
    8. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.
    9. Kai Gutenschwager & Christian Niklaus & Stefan Voß, 2004. "Dispatching of an Electric Monorail System: Applying Metaheuristics to an Online Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 38(4), pages 434-446, November.
    10. Fink, Andreas & Vo[ss], Stefan, 2003. "Solving the continuous flow-shop scheduling problem by metaheuristics," European Journal of Operational Research, Elsevier, vol. 151(2), pages 400-414, December.
    11. Van Breedam, Alex, 1995. "Improvement heuristics for the Vehicle Routing Problem based on simulated annealing," European Journal of Operational Research, Elsevier, vol. 86(3), pages 480-490, November.
    12. Maria da Conceição Cunha, 1999. "On Solving Aquifer Management Problems with Simulated Annealing Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 153-170, June.
    13. C. P. Stephens & W. Baritompa, 1998. "Global Optimization Requires Global Information," Journal of Optimization Theory and Applications, Springer, vol. 96(3), pages 575-588, March.
    14. Goodson, Justin C. & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2012. "Cyclic-order neighborhoods with application to the vehicle routing problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 217(2), pages 312-323.
    15. S Küçükpetek & F Polat & H Oğuztüzün, 2005. "Multilevel graph partitioning: an evolutionary approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 549-562, May.
    16. Stoica, R.S. & Gregori, P. & Mateu, J., 2005. "Simulated annealing and object point processes: Tools for analysis of spatial patterns," Stochastic Processes and their Applications, Elsevier, vol. 115(11), pages 1860-1882, November.
    17. Dell'Amico, Mauro & Trubian, Marco, 1998. "Solution of large weighted equicut problems," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 500-521, April.
    18. Schlereth, Christian & Stepanchuk, Tanja & Skiera, Bernd, 2010. "Optimization and analysis of the profitability of tariff structures with two-part tariffs," European Journal of Operational Research, Elsevier, vol. 206(3), pages 691-701, November.
    19. Orlin, James & Sharma, Dushyant, 2003. "The Extended Neighborhood: Definition And Characterization," Working papers 4392-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    20. Melissa Gama & Bruno Filipe Santos & Maria Paola Scaparra, 2016. "A multi-period shelter location-allocation model with evacuation orders for flood disasters," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 4(3), pages 299-323, 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_s10479-014-1771-7. 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.