AN INTEGRATED MODEL OF DATA ENVELOPMENT ANALYSIS AND ARTIFICIAL NEURAL NETWORKS FOR IMPROVING EFFICIENCY IN THE MUNICIPAL SOLID WASTE MANAGEMENT

ANTONELLA CAVALLIN; MARIANO FRUTOS; HERNÁN VIGIER; DIEGO G. ROSSIT

HANDBOOK OF RESEARCH ON EMERGENT APPLICATIONS OF OPTIMIZATION ALGORITHMS

IGI Global

Lugar: Hershey; Año: 2017; p. 206 - 231

In the last decades, the Integral Municipal Solid Waste Management (IMSWM) has become one of the most challenging issues for governmental authorities, which have struggled to lay down sustainable and financially stable policies for the sector. In this paper a model that evaluates the efficiency of the IMSWM through an approach that combines the technique Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN) is presented. Firstly, with DEA, City Hall?s are classified according to the efficiency of their garbage recollection systems. This is done in order to outline the modifications that are necessary for making garbage handling more efficient. Secondly, a ANN is used for predicting the necessary resources for obtaining what is defined as an ?efficient City Hall?. The results show the utility of the model for evaluating the relative efficiency and for guiding the improvement of the system.