IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-030-89795-6_6.html
   My bibliography  Save this book chapter

A Multi-attribute Decision-Making to Sustainable Construction Material Selection: A Bayesian BWM-SAW Hybrid Model

In: Advances in Best-Worst Method

Author

Listed:
  • Ramazan Alkan

    (Munzur University)

  • Melih Yucesan

    (Munzur University)

  • Muhammet Gul

    (Munzur University)

Abstract

The increase in urbanization and developments in the production industry has led to rapid progress in the construction sector. Many new strategies are developed in the industry to reduce costs and improve building quality. In recent years, the necessity of sustainable construction practices comes to the fore to renew the building stock damaged as a result of natural disasters and reduce the cost concerns that arise in this situation to a reasonable level. Due to limited resources and environmental concerns, researchers and practitioners have begun to develop sustainable building materials. The problem of selecting these materials when constructing a new building is vital. In particular, depending on the sector's rapid growth in Turkey, it is becoming more and more important to select the best sustainable construction material. Therefore, this paper proposes a model to evaluate the most appropriate sustainable construction material via two multi-attribute decision-making (MADM) methods called “Bayesian Best-Worst Method (BWM) and Simple Additive Weighting (SAW)”. Initially, the criteria derived from existing literature were evaluated with the aid of construction sector-based respondents and extra information about the interrelationship between the criteria were determined by credal ranking in Bayesian BWM. Then, via SAW, the most appropriate material was selected among a set of alternatives. Two cases regarding sustainable insulating material selection are considered for the demonstration of the proposed MADM model.

Suggested Citation

  • Ramazan Alkan & Melih Yucesan & Muhammet Gul, 2022. "A Multi-attribute Decision-Making to Sustainable Construction Material Selection: A Bayesian BWM-SAW Hybrid Model," Lecture Notes in Operations Research, in: Jafar Rezaei & Matteo Brunelli & Majid Mohammadi (ed.), Advances in Best-Worst Method, pages 67-78, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-89795-6_6
    DOI: 10.1007/978-3-030-89795-6_6
    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.

    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:lnopch:978-3-030-89795-6_6. 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.