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A robust ranking method extending ELECTRE III to hierarchy of interacting criteria,imprecise weights and stochastic analysis
Institution:1. Department of Operations, Innovation, and Data Sciences, ESADE Business School, Ramon Llull University, Torre Blanca Avenue, 59, 08172 Sant Cugat del Vallés, Barcelona, Spain;2. Automatic Control Department, BarcelonaTech, Vilanova i la Geltrú, Spain;1. Universidad Autónoma de Sinaloa, Mexico\n;2. CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Portugal;3. Université Paris-Dauphine, PSL Research University, CNRS (UMR 7243), LAMSADE, Paris, France;1. Department of Economics and Business, University of Catania, Corso Italia, 55, 95129, Catania, Italy;2. Portsmouth Business School, Centre for Operational Research and Logistics (CORL), University of Portsmouth, Portsmouth, United Kingdom;1. CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal;2. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal;3. Department of Economics and Business, University of Catania, Italy;4. CORL, Portsmouth Business School, University of Portsmouth, United Kingdom;1. Universidad Autónoma de Coahuila, Blvd. Revolución Oriente No. 151, 27000 Torreón, México;2. CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal;3. Universidad Autónoma de Sinaloa, Josefa Ortiz de Domínguez s/n, 80040 Culiacán, México
Abstract:A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.
Keywords:Multiple Criteria Hierarchy Process  ELECTRE III method  SRF method  Stochastic Multiobjective Acceptability Analysis
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