Routing optimization heuristics algorithms for urban solid waste transportation management

Karadimas, NV; Doukas, N; Kolokathi, M; Defteraiou, G

dc.contributor.author	Karadimas, NV	en
dc.contributor.author	Doukas, N	en
dc.contributor.author	Kolokathi, M	en
dc.contributor.author	Defteraiou, G	en
dc.date.accessioned	2014-03-01T01:57:11Z
dc.date.available	2014-03-01T01:57:11Z
dc.date.issued	2008	en
dc.identifier.issn	11092750	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28379
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-59249087880&partnerID=40&md5=5757af0ba6fbdf33a8e47af86cc342b9	en
dc.subject	Ant colony system	en
dc.subject	ArcGIS network analyst	en
dc.subject	Optimization algorithms	en
dc.subject	Routing	en
dc.subject	Simulation	en
dc.subject	Waste collection	en
dc.subject.other	Algorithms	en
dc.subject.other	Applications	en
dc.subject.other	Combinatorial mathematics	en
dc.subject.other	Combinatorial optimization	en
dc.subject.other	Geographic information systems	en
dc.subject.other	Highway planning	en
dc.subject.other	Motor transportation	en
dc.subject.other	Optimization	en
dc.subject.other	Planning	en
dc.subject.other	Solid wastes	en
dc.subject.other	Waste management	en
dc.subject.other	Ant colony system	en
dc.subject.other	ArcGIS network analyst	en
dc.subject.other	Optimization algorithms	en
dc.subject.other	Routing	en
dc.subject.other	Simulation	en
dc.subject.other	Waste collection	en
dc.subject.other	Routing algorithms	en
dc.title	Routing optimization heuristics algorithms for urban solid waste transportation management	en
heal.type	journalArticle	en
heal.publicationDate	2008	en
heal.abstract	During the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present paper, two individual meatheuristic algorithmic solutions, the ArcGIS Network Analyst and the Ant Colony System (ACS) algorithm, are introduced, implemented and discussed for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. Both proposed applications are based on a geo-referenced spatial database supported by a Geographic Information System (GIS). GIS are increasingly becoming a central element for coordinating, planning and managing transportation systems, and so in collaboration with combinatorial optimization techniques they can be used to improve aspects of transit planning in urban regions. Here, the GIS takes into account all the required parameters for the MSW collection (i.e. positions of waste bins, road network and the related traffic, truck capacities, etc) and its desktop users are able to model realistic network conditions and scenarios. In this case, the simulation consists of scenarios of visiting varied waste collection spots in the Municipality of Athens (MoA). The user, in both applications, is able to define or modify all the required dynamic factors for the creation of an initial scenario, and by modifying these particular parameters, alternative scenarios can be generated. Finally, the optimal solution is estimated by each routing optimization algorithm, followed by a comparison between these two algorithmic approaches on the newly designed collection routes. Furthermore, the proposed interactive design of both approaches has potential application in many other environmental planning and management problems.	en
heal.journalName	WSEAS Transactions on Computers	en
dc.identifier.volume	7	en
dc.identifier.issue	12	en
dc.identifier.spage	2022	en
dc.identifier.epage	2031	en