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Fuzzy extension of the CODAS method for multi-criteria market segment evaluation

    Mehdi Keshavarz Ghorabaee Affiliation
    ; Maghsoud Amiri Affiliation
    ; Edmundas Kazimieras Zavadskas Affiliation
    ; Reyhaneh Hooshmand Affiliation
    ; Jurgita Antuchevičienė Affiliation

Abstract

One of the important activities of a company that can increase its competitiveness is market segment evaluation and selection (mse/mss). We can usually consider mse/mss as a multi-criteria decision-making (mcdm) problem, and so we need to use an mcdm method to handle it. Uncertainty is one of the important factors that can affect the process of decision-making. Fuzzy mcdm approached have been designed to deal with the uncertainty of decision-making problems. In this study, a fuzzy extension of the codas (combinative distance-based assessment) method is proposed to solve multi-criteria group decision-making problems. We use linguistic variables and trapezoidal fuzzy numbers to extend the codas method. The proposed fuzzy codas method is applied to an example of market segment evaluation and selection problem under uncertainty. To validate the results, a comparison is performed between the fuzzy codas and two other mcdm methods (fuzzy edas and fuzzy topsis). A sensitivity analysis is also carried out to demonstrate the stability of the results of the fuzz codas. For this aim, ten sets of criteria weights are randomly generated and the example is solved using each set separately. The results of the comparison and the sensitivity analysis show that the proposed fuzzy codas method gives valid and stable results.

Keyword : market segment evaluation, market segment selection, MCDM, decision-making, fuzzy MCDM, fuzzy CODAS

How to Cite
Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antuchevičienė, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1-19. https://doi.org/10.3846/16111699.2016.1278559
Published in Issue
Feb 5, 2017
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