Genetic Algorithms for Municipal Solid Waste Collection and Routing Optimization

  • Nikolaos V. Karadimas
  • Katerina Papatzelou
  • Vassili G. Loumos
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


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.


Genetic Algorithm Solid Waste Municipal Solid Waste Travel Salesman Problem Travel Salesman Problem 


  1. 1.
    Municipality Of Athens. Estimation, Evaluation and Planning Of Actions for Municipal Solid Waste Services During Olympic Games 2004. Athens, Greece. (2003)Google Scholar
  2. 2.
    V. Maniezzo, Algorithms for large directed CARP instances: urban solid waste collection operational support, Technical Report UBLCS-2004-16, (2004).Google Scholar
  3. 3.
    S.K. Amponsah and S. Salhi, The investigation of a class of capacitated arc routing problems: the collection of garbage in developing countries, Waste Management 24, 711–721,(2004).CrossRefGoogle Scholar
  4. 4.
    G. Ghiani, F. Guerriero, G. Improta and R. Musmannod, Waste collection in Southern Italy: solution of a real-life arc routing problem, Intl. Trans, in Op. Res. 12, 135–144, (2005).CrossRefGoogle Scholar
  5. 5.
    S. Sahoo, S. Kim and B.I. Kim, Routing Optimization for Waste Management, Interfaces Journal (SCIE), Informs (OR/MS), 35(1), 24–36, (2005).Google Scholar
  6. 6.
    B. Bullnheimer, R.F. Hartl, and C. Strauss, Applying the ant system to the vehicle routing problem. In: Osman, I.H., Voss, S., Martello, S. & Roucairol C. (eds): Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1998), pp. 109–120.Google Scholar
  7. 7.
    A.V. Donati, R. Montemanni, N. Casagrande, A.E. Rizzoli, and L.M. Gambardella, Time-dependent Vehicle Routing Problem with a Multi Ant Colony System, Technical Report TR-17-03, IDSIA, Galleria 2, Manno, Switzerland (2003).Google Scholar
  8. 8.
    M. Dorigo, V. Maniezzo and A. Colorni, The ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, 26(1), 1–13, (1996).Google Scholar
  9. 9.
    M. Dorigo and L.M. Gambardella, Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation, 1(1) (1997).Google Scholar
  10. 10.
    N.V. Karadimas, G. Kouzas, I. Anagnostopoulos and V. Loumos, Urban Solid Waste Collection and Routing: The Ant Colony Strategic Approach. International Journal of Simulation: Systems, Science & Technology, 6(12–13), 45–53, (2005).Google Scholar
  11. 11.
    N.V. Karadimas, K. Papatzelou and V.G. Loumos, Optimal solid waste collection routes identified by the ant colony system algorithm, Waste Management & Research 25, 139–147, (2007).CrossRefGoogle Scholar
  12. 12.
    P. Viotti, A. Pelettini, R. Pomi and C. Innocetti, Genetic algorithms as a promising tool for optimisation of the MSW collection routes, Waste Management Research, 21, 292–298, (2003).CrossRefGoogle Scholar
  13. 13.
    I. von Poser, A.R. Awad, Optimal Routing for Solid Waste Collection in Cities by Using Real Genetic Algorithm, Information and Communication Technologies, ICTTA’ 06, 1, 221–226,(2006).Google Scholar
  14. 14.
    J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence, MIT Press, 1992. First Published by University of Michigan Press 1975.Google Scholar
  15. 15.
    N.V. Karadimas, O. Mavrantza and V. Loumos, GIS Integrated Waste Production Modelling. IEEE EUROCON 2005-The International Conference on “Computer as a Tool”, Belgrade, 22–24 November, pp. 1279–1282. Serbia & Montenegro, (2005).Google Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Nikolaos V. Karadimas
    • 1
  • Katerina Papatzelou
    • 1
  • Vassili G. Loumos
    • 1
  1. 1.School of Electrical and Computer Engineering, Multimedia Technology LaboratoryNational Technical University of AthensAthensGreece

Personalised recommendations