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Determining new possible weight values in PROMETHEE: a procedure based on data envelopment analysis

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Journal of the Operational Research Society

Abstract

Operational research (OR) offers efficient tools to support managers in strategic decision-making processes. Data envelopment analysis (DEA) and multiple criteria decision aid (MCDA) are two important research areas in OR. These two domains are both based on the evaluation of “objects” according to multiple “points of views”. Within the MCDA framework, choosing appropriate weights for the different criteria often arises as a problem itself for decision makers. As a consequence, researchers have developed original methodologies to help them during this elicitation phase. In this work, we aim to investigate how DEA can be used to propose weights in the context of the PROMETHEE II method. More precisely, we suggest an extension of the so-called “decision maker brain” used in the GAIA plane (also known as PROMETHEE VI) based on DEA. The underlying idea is based on the computation of weights in PROMETHEE (GAIA brain) which are compatible with the DEA analysis. We end this paper with a numerical example.

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Authors Contribution

This paper is a contribution of Operations Research methodologies in Data Envelopment Analysis and Multi-Criteria Decision Aid to help decision makers on the computation of weights in PROMETHEE technique. It tries to give a first vision to decision makers to choose weights of criteria. It is based on GAIA brain in PROMETHEE which is expressed as decision maker brain. The proposed approach used DEA to generate GAIA brain.

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  1. SMG Unit, Computer and Decision Engineering Department, École polytechnique de Bruxelles, Université libre de Bruxelles, Boulevard du Triomphe, CP 210-01, 1050, Ixelles, Brussels, Belgium

    Maryam Bagherikahvarin & Yves De Smet

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  1. Maryam Bagherikahvarin
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  2. Yves De Smet
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Correspondence to Maryam Bagherikahvarin.

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Bagherikahvarin, M., De Smet, Y. Determining new possible weight values in PROMETHEE: a procedure based on data envelopment analysis. J Oper Res Soc 68, 484–495 (2017). https://doi.org/10.1057/s41274-016-0107-1

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