Solid Waste Collection in Ciudad Universitaria-UNAM Using a VRP Approach and Max-Min Ant System Algorithm

  • Katya Rodriguez-VazquezEmail author
  • Beatriz Aurora GarroEmail author
  • Elizabeth ManceraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)


The collection of solid waste is a very important problem for most of the modern cities of the world. The solution to this problem requires to apply optimization techniques capable of design the best path routes that guarantee to collect all the waste minimizing the cost. Several computation techniques could be applied to solve this problem and one of the most suitable could be swarm optimization such as ant colony optimization. In this paper, we propose a methodology for searching a set of collection paths of solid waste that optimize the distance of a tour in Ciudad Universitaria (UNAM). This methodology uses a vehicle routing problem (VRP) approach combined with Max-Min Ant System algorithm. To assess the accuracy of the proposal, we select the scholar circuit in the area of Ciudad Universitaria. The results shown a shortest distance travelled and better distribution than the empiric route used actually for the cleaning service.


Vehicle routing problem (VRP) Max-Min Ant System (Max-Min AS) algorithm Collection of solid waste Optimization path routes 



The authors would like to thank Dirección General de Obras y Conservación, UNAM. This work was supported by the SECITI under Project SECITI/064/2016.


  1. 1.
    Bonomo, F., Durán, G., Larumbe, F., Marenco, J.: A method for optimiz-ing waste collection using mathematical programming: a Buenos Aires case study. Waste Manag. Res. 30(3), 311–324 (2012)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Birattari, M., Stützle, T.: Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1, 28–39 (2006)CrossRefGoogle Scholar
  3. 3.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: an autocatalytic optimizing process. Université Libre de Bruxelles (1991)Google Scholar
  5. 5.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Dip. Electtronica e Informazion, Politecnico di Milano Italy (1992)Google Scholar
  6. 6.
    Filipiak, K.A., Abdel-Malek, L., Hsieh, H.-N., Meegoda, J.N.: Optimization of municipal solid waste collection system: case study. Pract. Period. Hazard. Toxic Radioact. Waste Manag. 13(3), 210–216 (2009)CrossRefGoogle Scholar
  7. 7.
    Hemmelmayr, V., et al.: A heuristic solution method for node routing based solid waste collection problems. J. Heuristics 19(2), 129–156 (2013)CrossRefGoogle Scholar
  8. 8.
    Hornig, E.S., Fuentealba, N.R.: Modelo ACO para la recolección de residuos por contenedores. Rev. Chil. Ing. 17(2), 236–243 (2009)Google Scholar
  9. 9.
  10. 10.
    Ismail, Z., Loh, S.: Ant colony optimization for solving solid waste collection scheduling problems. J. Math. Stat. 5(3), 199 (2009)CrossRefGoogle Scholar
  11. 11.
    Karadimas, N.V., et al.: Optimal solid waste collection routes identified by the ant colony system algorithm. Waste Manag. Res. 25(2), 139–147 (2007)CrossRefGoogle Scholar
  12. 12.
    Kulcar, T.: Optimizing solid waste collection in Brussels. Eur. J. Oper. Res. 90(1), 71–77 (1996)CrossRefGoogle Scholar
  13. 13.
    Mansini, R., et al.: A linear programming model for the separate refuse collection service. Comput. Oper. Res. 25(7), 659–673 (1998)CrossRefGoogle Scholar
  14. 14.
    Mourao, M., Almeida, M.T.: Lower-bounding and heuristic methods for a refuse collection vehicle routing problem. Eur. J. Oper. Res. 121(2), 420–434 (2000)CrossRefGoogle Scholar
  15. 15.
    Ogwueleka, T.C.: Route optimization for solid waste collection: Onitsha (Nigeria) case study. J. Appl. Sci. Environ. Manag. 13(2), 37–40 (2009)Google Scholar
  16. 16.
    Stützle, T., Hoos, H.H.: Improving the ant system: a detailed report on the MAX– MIN ant system. Technical report AIDA–96–12, FG Intellektik, FB Informatik, TU Darmstadt, Germany, August 1996 (1996)Google Scholar
  17. 17.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  18. 18.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.IIMAS, UNAM, Ciudad UniversitariaMexico CityMexico

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