Managing Urban Waste Collection through Timed Automata Based Fuzzy Cognitive Maps

In the last years, the increasing urbanization has constrained to face the dramatic growth of the urban waste production and the consequent socio-economical and environmental impact. The relevance of finding an optimal waste management further increases when it involves hazardous materials, since they represent a vulnerable infrastructure sector for homeland defense. Although there is a general agreement on the best strategies for solving urban garbage problem, an opportune waste management seems far due to its intrinsic complexity arising from necessity of dealing with several factors which are often in conflict each other. Over the years, several computerized waste management systems, including deterministic models and fuzzy approaches, have been developed aimed at addressing this complex problem. However, all these approaches do not consider relevant factors which could affect decision policies related to waste treatment, i.e., the rapid evolutions and modifications occurring in a complex scenario such as the urban environment. In order to overcome this drawback, this paper presents an innovative waste management simulation system based on a new timed cognitive inference engine, named Timed Automata based Fuzzy Cognitive Map (TAFCM). A TAFCM is able to simulate the dynamic features of a waste management environment thanks to its temporal benefits due to its ability of dealing with the concept of time in a direct way. As shown in the experimental section, TAFCMs represent a suitable and efficient methodology to manage the waste production problem.