Ant Colony Route Optimization for Municipal Services

In the present paper the Ant Colony Optimization (ACO) Algorithm is introduced for best routing identification applied in urban solid waste collection. The proposed solid waste management system is based on a geo-referenced Spatial Database supported by a Geographic Information System (GIS). The GIS takes account of all the required parameters for solid waste collection. These parameters involve static and dynamic data, such as positions of trash-cans, road network, related traffic and population density, In addition, time schedule of trash-collection workers and track capacities and technical characteristics are considered. ACO spatio-temporal statistical analysis model is used to estimate interrelations between dynamic factors, like network traffic changes in residential and commercial areas in a 24 hour schedule, and to produce optimized solutions. The user, in the proposed system, is able to define or modify all required dynamic factors for the creation of an initial scenario. By modifying these particular parameters, alternative scenarios can be generated leading to the several solutions. The Optimal solution is identified by a cost function that takes into account various parameters, for instance labor and equipment costs as well as social implications.