Non Additive Robust Ordinal Regression for urban and territorial planning: an application for siting an urban waste landfill

Non Additive Robust Ordinal Regression for urban and territorial planning: an application for siting an urban waste landfill

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Article ID: iaor20163542
Volume: 245
Issue: 1
Start Page Number: 427
End Page Number: 456
Publication Date: Oct 2016
Journal: Annals of Operations Research
Authors: , , , , ,
Keywords: government, decision, statistics: regression, decision theory: multiple criteria
Abstract:

In this paper we deal with an urban and territorial planning problem by applying the Non Additive Robust Ordinal Regression (NAROR). NAROR is a recent extension of the Robust Ordinal Regression family of Multiple Criteria Decision Aiding methods to the Choquet integral preference model which permits to represent interaction between considered criteria through the use of a set of non‐additive weights called capacity or fuzzy measure. The use of NAROR permits the Decision Maker (DM) to give preference information in terms of preferences between pairs of alternatives with which she is familiar, and relative importance and interaction of considered criteria. The basic idea of NAROR is to consider the whole set of capacities that are compatible with the preference information given by the DM. In fact, the recommendation supplied by NAROR is expressed in terms of necessary preferences, in case an alternative is preferred to another for all compatible capacities, and of possible preferences, in case an alternative is preferred to another for at least one compatible capacity. In the considered case study, several sites for the location of a landfill are analyzed and compared through the use of the NAROR on the basis of different criteria, such as presence of population, hydrogeological risk, interferences on transport infrastructures and economic cost. This paper is the first application of NAROR to a real‐world problem, even if not already with real DMs, but with a panel of experts simulating the decision process.

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