A simulation based multi-attribute group decision making technique with decision constraints

Bayram H., Şahin R.

APPLIED SOFT COMPUTING, cilt.49, ss.629-640, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.asoc.2016.08.049
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.629-640
  • Anahtar Kelimeler: Multi-attribute group decision making, TOPSIS, Triangular distribution, Absolute judgments, Monte Carlo simulation, Decision constraints, FUZZY TOPSIS METHOD, SYSTEM SELECTION, SUPPLIER SELECTION, PROJECT SELECTION, ROBOT SELECTION, AHP, METHODOLOGY, ENVIRONMENT, MANAGEMENT, CHAIN
  • Gazi Üniversitesi Adresli: Evet

Özet

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a useful technique for solving Multi Attribute Group Decision Making (MAGDM) problems. In MAGDM, the performance scores of the alternatives and the weights of assessment attributes are mostly vague. Therefore, using of deterministic data throughout decision making process may lead to inaccurate results. In order to overcome inherent vagueness and uncertainty, various fuzzy MAGDM techniques were presented in the literature. However, these fuzzy MAGDM techniques are focused on expected and extreme values, which are sometimes insufficient for the precise determination of alternatives preference structure. In this paper, in order to eliminate the limitations of deterministic and fuzzy MAGDM methods, we present a probabilistic methodology, which is based on TOPSIS and Monte-Carlo simulation of triangular data. In addition to its straightforward application and thanks to its versatility, simulation enables decision makers to incorporate some decision constraints into decision-making process. Two illustrative examples are also given to show the effectiveness of the proposed methodology. The method is also compared with a fuzzy TOPSIS technique from the literature. (C) 2016 Published by Elsevier B.V.