IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-02726-1_7.html
   My bibliography  Save this book chapter

The SIMUS Method

In: Strategic Approach in Multi-Criteria Decision Making

Author

Listed:
  • Nolberto Munier

    (INGENIO, Polytechnic University of Valencia)

  • Eloy Hontoria

    (Universidad Politécnica de Cartagena)

  • Fernando Jiménez-Sáez

    (Universidad Politécnica de Valencia)

Abstract

This chapter aims at explaining the SIMUS method, trying to show without formulas how it works. Its purpose is to illustrate the DM about its principles and characteristics for him/her to understand and apply it without going into complex mathematical demonstrations. That is, one thing is to understand a method and to know how to use it and how to get the most from it and another is to be knowledgeable about its mathematical intricacies. SIMUS is a hybrid method based on linear programming, weighted sum and outranking methods. If the reader is interested or perhaps rather curious about how LP works, in the Appendix is a detailed and accessible explanation. Since SIMUS is also grounded on the two above-mentioned techniques, it produces two results but with the same ranking. It is the equivalent of solving a problem with two distinctive methods and getting coincident rankings. Naturally, it does not mean that SIMUS delivers the ‘true’ solution, if it exists, but these two similar outputs offer a good deal of reliability. Although SIMUS is a heuristic method, the compromising solution obtained is based on the Pareto efficient matrix. An application example illustrates how to load the data into the SIMUS software and shows its operation. The chapter continues explaining how to incorporate especial and real-world issues in the model and ends examining why both LP and SIMUS do not produce rank reversal.

Suggested Citation

  • Nolberto Munier & Eloy Hontoria & Fernando Jiménez-Sáez, 2019. "The SIMUS Method," International Series in Operations Research & Management Science, in: Strategic Approach in Multi-Criteria Decision Making, chapter 0, pages 117-157, Springer.
  • Handle: RePEc:spr:isochp:978-3-030-02726-1_7
    DOI: 10.1007/978-3-030-02726-1_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    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:isochp:978-3-030-02726-1_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.

    We have no bibliographic references for this item. You can help adding them by using 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.