IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v54y2003i9d10.1057_palgrave.jors.2601580.html
   My bibliography  Save this article

An evolutionary approach for the target allocation problem

Author

Listed:
  • E Erdem

    (Atilim University)

  • N E Ozdemirel

    (Middle East Technical University)

Abstract

We propose an evolutionary approach for target allocation in tactical level land combat. The purpose is to assign friendly military units to enemy units such that the total weapon effectiveness used is minimised while the attrition goals set for the enemy units are satisfied. A repair algorithm is developed to ensure feasibility with respect to the attrition goal constraints. A tightness measure is devised to determine the population size of the genetic algorithm as a function of constraint tightness. Also, a local improvement algorithm is used to further improve the solution quality. Experimental results indicate that the genetic algorithm can find solutions with acceptable quality in reasonable computation time. Although the approach is developed for the target allocation problem, it can be adapted for other assignment problems.

Suggested Citation

  • E Erdem & N E Ozdemirel, 2003. "An evolutionary approach for the target allocation problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(9), pages 958-969, September.
  • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:9:d:10.1057_palgrave.jors.2601580
    DOI: 10.1057/palgrave.jors.2601580
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2601580
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/palgrave.jors.2601580?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. Gong, Dijin & Yamazaki, Genji & Gen, Mitsuo & Xu, Weixuan, 1999. "A genetic algorithm method for one-dimensional machine location problems," International Journal of Production Economics, Elsevier, vol. 60(1), pages 337-342, April.
    2. Herrera, Francisco & Lopez, Enrique & Mendana, Cristina & Rodriguez, Miguel A., 1999. "Solving an assignment-selection problem with verbal information and using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 119(2), pages 326-337, December.
    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. Cha, Young-Ho & Kim, Yeong-Dae, 2010. "Fire scheduling for planned artillery attack operations under time-dependent destruction probabilities," Omega, Elsevier, vol. 38(5), pages 383-392, October.
    2. N E Ozdemirel & L Kandiller, 2006. "Semi-dynamic modelling of heterogeneous land combat," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 38-51, January.
    3. Anissa Frini & Adel Guitouni & Abderrezak Benaskeur, 2017. "Solving Dynamic Multi-Criteria Resource-Target Allocation Problem Under Uncertainty: A Comparison of Decomposition and Myopic Approaches," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1465-1496, November.

    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. Guillaume, Romain & Houé, Raymond & Grabot, Bernard, 2014. "Robust competence assessment for job assignment," European Journal of Operational Research, Elsevier, vol. 238(2), pages 630-644.
    2. Vila Goncalves Filho, Eduardo & Jose Tiberti, Alexandre, 2006. "A group genetic algorithm for the machine cell formation problem," International Journal of Production Economics, Elsevier, vol. 102(1), pages 1-21, July.
    3. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.

    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:pal:jorsoc:v:54:y:2003:i:9:d:10.1057_palgrave.jors.2601580. 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.palgrave-journals.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.