HEAL DSpace

Municipal waste collection of large items optimized with arc gis Network Analyst

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dc.contributor.author Karadimas, NV en
dc.contributor.author Kolokathi, M en
dc.contributor.author Defteraiou, G en
dc.contributor.author Loumos, V en
dc.date.accessioned 2014-03-01T02:51:08Z
dc.date.available 2014-03-01T02:51:08Z
dc.date.issued 2007 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/35387
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-84857558527&partnerID=40&md5=1e0b2abcfa6f9f223bcca9fe739c15b6 en
dc.subject ArcGis en
dc.subject Geographical information system en
dc.subject Large items en
dc.subject Optimization en
dc.subject Simulation en
dc.subject Solid waste en
dc.subject Waste collection en
dc.subject.other ArcGis en
dc.subject.other Geographical Information System en
dc.subject.other Large items en
dc.subject.other Simulation en
dc.subject.other Waste collection en
dc.subject.other Accidents en
dc.subject.other Automobiles en
dc.subject.other Geographic information systems en
dc.subject.other Optimization en
dc.subject.other Solid wastes en
dc.title Municipal waste collection of large items optimized with arc gis Network Analyst en
heal.type conferenceItem en
heal.publicationDate 2007 en
heal.abstract In the present paper the ArcGIS Network Analyst Algorithm is introduced for best routing identification applied in municipal waste collection of large items. The proposed application takes into account all the required parameters for the waste collection of large items so as its desktop users to be able to model realistic network conditions and scenarios. In this case, the simulation consists of scenarios of visiting loading spots in the municipality of Athens, in order to collect large items that couldn't be collected by the standard waste collection trucks, due to size and other prohibitive obstacles. The Network Analyst is used to estimate interrelations between the dynamic factors, like network traffic changes (closed roads due to natural or technical causes, for example, fallen trees, car accidents, etc) in the area under study and to produce optimized solutions. The user 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 leading to several solutions. Finally, the optimal solution is identified by a function that takes into consideration various parameters, for example the shortest distance, road network as well as social and environmental implications. © 2007 ECMS. en
heal.journalName 21st European Conference on Modelling and Simulation: Simulations in United Europe, ECMS 2007 en
dc.identifier.spage 80 en
dc.identifier.epage 85 en


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