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The Synergies Between Data Envelopment Analysis and Multi-Criteria Decision Aid: Case of the PROMETHEE Method

par Bagherikahvarin, Maryam
Président du jury Bersini, Hugues
Promoteur De Smet, Yves
Publication Non publié, 2017-07-04

Thèse de doctorat

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Résumé :

For a little less than twenty years, researchers have worked on integrating Data Envelopment Analysis (DEA) and Multi-Criteria Decision Aid (MCDA). Several contributions have been done by integrating DEA with different MCDA methods to bring this field to what it is today. After studying the course of Multi-Criteria Data Envelopment Analysis (MCDEA) integration through numerous works, the future of such an attempt can be questionable. For this aim, the PROMETHEE method in MCDA has been integrated with DEA. To the best of our knowledge, this synergy has been done for the first time in this thesis.Two synergies have been conducted: Using PROMETHEE in DEA and vice versa. The first contribution applies PROMETHEE in DEA to develop a new weight restricted DEA model. This new model has two main characteristics: more discrimination power between efficient units and engaging a priori information of decision makers in DEA. The second contribution uses both DEA and PROMETHEE to propose a new ranking technique. DEA is employed to generate a pairwise comparison matrix to be used in PROMETHEE for the purpose of ranking alternatives. The last contribution uses DEA in PROMETHEE. It presents a new algorithm to propose weights in the context of the PROMETHEE II method based on DEA. Furthermore, these two methods can be used in parallel. Comparing the results obtained from DEA and PROMETHEE in evaluating the performance of units enriches the analysis of decision-making problem by confirming the robustness of answers. The purpose of this integration is to provide some tools to help decision makers in the process of evaluating the performance of alternatives and analyzing the multicriteria decision-making problems.