Urban Solid Waste Collection and Routing: the Ant Colony Strategic Approach

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 into account 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 schedules of trash-collection workers, track capacities and technical characteristics are considered. The ACO spatiotemporal 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 of the required dynamic factors for the creation of an initial scenario. By modifying these particular parameters, alternative scenarios can be generated leading to several solutions. The objective of the proposed system is to identify the most cost-effective alternative scenario for waste collection and transport, to estimate its running cost and to simulate its application.