Hesitant Fuzzy MADM Approach in Optimal Selection of Investment Projects

12 pages•Published: December 18, 2015

Irina Khutsishvili, Gia Sirbiladze and Gvanca Tsulaia

Abstract

The article proposes a multi-attribute decision making (MADM) approach, which is applied to the problem of optimal selection of the investment projects. This novel methodology comprises two stages. First, it makes ranking of projects based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method presented in hesitant fuzzy environment. We consider the case when the information on the weights of the attributes is completely unknown. The identification of the weights of the attributes is made in the context of hesitant fuzzy sets and is based on the De Luca-Termini information entropy. The ranking of alternatives is made in accordance with the proximity of their distance to the positive and negative ideal solutions. Second stage of the methodology allows making the most profitable investment in several projects simultaneously. The decision on an optimal distribution of allocated investments among the selected projects is provided using the method developed by the authors for a possibilistic bicriteria optimization problem. An investment example is given to illustrate the application of the proposed approach.

Keyphrases: hesitant fuzzy set, information entropy, mathematical programming problem, multi attribute decision making, ranking of investment projects, topsis method

In: Georg Gottlob, Geoff Sutcliffe and Andrei Voronkov (editors). GCAI 2015. Global Conference on Artificial Intelligence, vol 36, pages 151-162.

Links:	https://easychair.org/publications/paper/qwmZ
	https://doi.org/10.29007/gnfq

BibTeX entry

@inproceedings{GCAI2015:Hesitant_Fuzzy_MADM_Approach,
  author    = {Irina Khutsishvili and Gia Sirbiladze and Gvanca Tsulaia},
  title     = {Hesitant Fuzzy MADM Approach in Optimal Selection of Investment Projects},
  booktitle = {GCAI 2015. Global Conference on Artificial Intelligence},
  editor    = {Georg Gottlob and Geoff Sutcliffe and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {36},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/qwmZ},
  doi       = {10.29007/gnfq},
  pages     = {151-162},
  year      = {2015}}

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