The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 31-698
Full Name: Nikolaos Karadimas
Position: Senior Lecturer
Age: ON
Sex: Male
Address: Multimedia Technology Laboratory National Technical University of Athens (NTUA) 9 Heroon Polytechneiou, Zografou Campus, 157 80 Athens
Country: GREECE
Tel: 6932121552
Tel prefix: +30
Fax:
E-mail address: nkaradimas@medialab.ntua.gr
Other E-mails: nkaradim@central.ntua.gr
Title of the Paper: Routing Optimization Heuristics Algorithms for Urban Solid Waste Transportation Management
Authors as they appear in the Paper: Nikolaos V. Karadimas, Nikolaos Doukas, Maria Kolokathi, Gerasimoula Defteraiou
Email addresses of all the authors: nkaradimas@medialab.ntua.gr, nikolaos@doukas.net.gr, el00666@mail.ntua.gr, el00661@mail.ntua.gr
Number of paper pages: 10
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.
Keywords: Ant Colony System, ArcGIS Network Analyst, Waste Collection, Optimization Algorithms, Routing, Simulation.
EXTENSION of the file: .doc
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