|Start Page Number:||3|
|End Page Number:||23|
|Publication Date:||Jan 2014|
|Journal:||Journal of Multi-Criteria Decision Analysis|
|Authors:||Greco Salvatore, Bottero Marta, Abastante Francesca, Lami Isabella|
The location problems of the so‐called undesirable facilities such as waste incinerators or landfills presents two main characteristics: (i) social opposition and (ii) a huge number of social, economic and environmental data that have to be taken into account. In this sense, the decision problem can be seen as an intrinsically complex problem because it involves different interconnected elements and must achieve objectives that are often in conflict. The work proposes the use of the theory of the dominance‐based rough set approach (DRSA). The DRSA is a development of the rough sets philosophy which concerns the possibility of extending this theory to multiple criteria decision analysis by providing the model with preference‐ordered aspects of the problem. This article presents an argument for the use of DRSA in decisions‐aiding processes concerning urban and territorial projects. Mention has to be made to the fact that there are very few applications of DRSA to real case studies, and in particular, there is no application concerning the localization of undesirable facilities. Given the very delicate nature of the problem, where the contrasts are not only theoretical but also concrete, it is essential to ‘test’ the various methods of decision support before using them in real contexts. In this article, we argue that a simulation phase should reclaim responsibility for reliability and validity by implementing verification and self‐correcting strategies during the development of the application itself. In particular, the paper presents an application to a real‐world problem, concerning the choice of the location for a waste incinerator in the Province of Turin (Italy). In the application, different sites are analysed and compared through the use of the DRSA on the basis of different attributes, such as presence of population, hydrogeological risk, landscape value, interferences on transport infrastructures.