In the present paper, an innovative model for the estimation of municipal solid waste generation and collection is proposed. This model is part of an extended solid waste management system and uses a spatial Geodatabase, integrated in a GIS environment. It takes into consideration several parameters of waste generation, such as population density, commercial activities, road characteristics and their influence on the location and allocation of waste bins. Ground-based analysis was applied for the estimation of the inter-relations between the aforementioned factors and the variations in waste generation between residential and commercial areas. Therefore, the proposed model follows a unified categorization approach for residential and commercial activities and focuses on the dominant factors that determine waste generation in the area under study. The most important result of the research work presented in the current paper is an accurate estimation of the optimal number of waste bins and their allocation. A new methodology and an appropriate algorithm have been developed for this purpose in order to facilitate routing and waste collection. By using these results, municipalities aware of social, economical and environmental factors, related to waste management, can achieve optimal usage of their resources and offer the best possible services to their citizens.

In the present paper, the Ant Colony System (ACS) algorithm is used for the identification of optimal routes in the case of municipal solid waste (MSW) collection. The proposed MSW 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 include static and dynamic data, such as the positions of waste bins, the road network and the related traffic, as well as the population density in the area under study. In addition, waste collection schedules, truck capacities and their characteristics are also taken into consideration. Spatio-temporal statistical analysis is used to estimate inter-relations between dynamic factors, like network traffic changes in residential and commercial areas. The user, in the proposed system, is able to define or modify all of the required dynamic factors for the creation of alternative initial scenarios. The objective of the system is to identify the most cost-effective scenario for waste collection, to estimate its running cost and to simulate its application. Finally, the results of the ACS algorithm are compared with the empirical method currently used by the Municipality of Athens.

In the present paper, the Genetic Algorithm (GA) is used for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. The identification of a route for MSW collection trucks is critical since it has been estimated that, of the total amount of money spent for the collection, transportation, and disposal of solid waste, approximately 60-80% is spent on the collection phase. Therefore, a small percentage improvement in the collection operation can result to a significant saving in the overall cost. The proposed MSW 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 include static and dynamic data, such as the positions of waste bins, the road network and its related traffic, as well as the population density in the area under study. In addition, waste collection schedules, truck capacities and their characteristics are also taken into consideration. Spatiotemporal statistical analysis is used to estimate interrelations between dynamic factors, like network traffic changes in residential and commercial areas. The user, in the proposed system, is able to define or modify all of the required dynamic factors for the creation of altemative initial scenarios. The objective of the system is to identify the most cost-effective scenario for waste collection, to estimate its running cost and to simulate its apphcation.

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