Abstract
The Analytical Hierarchy Process (AHP) is arguably the most popular and factual approach for computing the weights of attributes in the multi-attribute decision-making environment. The Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) is an outranking family of multi-criteria decision-making techniques for evaluating a finite set of alternatives, that relies on multiple and inconsistent criteria. One of its main advantages is the variety of admissible preference functions that can measure the differences between alternatives, in response to the type and nature of the criteria. This research article studies a version of the PROMETHEE technique that encompasses multipolar assessments of the performance of each alternative (relative to the relevant criteria). As is standard practice, first we resort to the AHP technique in order to quantify the normalized weights of the attributes by the pairwise comparison of criteria. Afterwards the m-polar fuzzy PROMETHEE approach is used to rank the alternatives on the basis of conflicting criteria. Six types of generalized criteria preference functions are used to measure the differences or deviations of every pair of alternatives. A partial ranking of alternatives arises by computing the positive and negative outranking flows of alternatives, which is known as PROMETHEE I. Furthermore, a complete ranking of alternatives is achieved by the inspection of the net flow of alternatives, and this is known as PROMETHEE II. Two comparative analysis are performed. A first study checks the impact of different types of preference functions. It considers the usual criterion preference function for all criteria. In addition, we compare the technique that we develop with existing multi-attribute decision-making methods.
Highlights
This paper contributes to the extensive and important literature on multi-criteria decision analysis (MCDA)
One owes to the use of a multi-criteria utility function. This approach includes the technique for the order of preference by similarity to an ideal solution (TOPSIS) [6] and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) meaning multi-criteria optimization and compromise solution [7], among others
We present a review of different types of preference functions corresponding to generalized criteria that are frequently used in the applications of the PROMETHEE method
Summary
This paper contributes to the extensive and important literature on multi-criteria decision analysis (MCDA). For the last few decades, a variety of MADM methods have helped decision makers to design the framework and determine the solutions that best suit the goals of their decision-making problems having multiple criteria They include the aforementioned AHP and the more general analytical network process (ANP) [21], data envelopment analysis (DEA) [22], grey theory [23], etc. Ziemba [41] introduced a new MCDM technique by suggesting the NEAT F-PROMETHEE approach in which the results are obtained by using the trapezoidal fuzzy numbers [42] All these existing versions of PROMETHEE technique are useful and appropriate when the decision data is in the form of precise information or fuzzy imprecision, but cannot be applied to multi-polar imprecise information.
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