Genetic algorithms for municipal solid waste collection and routing optimization

Karadimas, NV; Papatzelou, K; Loumos, VG

dc.contributor.author	Karadimas, NV	en
dc.contributor.author	Papatzelou, K	en
dc.contributor.author	Loumos, VG	en
dc.date.accessioned	2014-03-01T02:44:38Z
dc.date.available	2014-03-01T02:44:38Z
dc.date.issued	2007	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31924
dc.subject	Cost Effectiveness	en
dc.subject	Dynamic Data	en
dc.subject	Genetic Algorithm	en
dc.subject	Geographic Information System	en
dc.subject	Management System	en
dc.subject	Municipal Solid Waste	en
dc.subject	Network Traffic	en
dc.subject	Optimal Routing	en
dc.subject	Population Density	en
dc.subject	Road Network	en
dc.subject	Route Optimization	en
dc.subject	Solid Waste	en
dc.subject	Spatial Database	en
dc.subject	Statistical Analysis	en
dc.title	Genetic algorithms for municipal solid waste collection and routing optimization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-0-387-74161-1_24	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-0-387-74161-1_24	en
heal.publicationDate	2007	en
heal.abstract	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 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. © 2007 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/978-0-387-74161-1_24	en
dc.identifier.volume	247	en
dc.identifier.spage	223	en
dc.identifier.epage	231	en