An NSGA-II Algorithm for the Green Vehicle Routing Problem

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7245)


In this paper, we present and define the bi-objective Green Vehicle Routing Problem GVRP in the context of green logistics. The bi-objective GVRP states for the problem of finding routes for vehicles to serve a set of customers while minimizing the total traveled distance and the co 2 emissions. We review emission factors and techniques employed to estimate co 2 emissions and integrate them into the GVRP definition and model. We apply the NSGA-II evolutionary algorithm to solve GVRP benchmarks and perform statistical analysis to evaluate and validate the obtained results. The results show that the algorithm obtain good results and prove the explicit interest grant to emission minimization objective.


Green vehicle routing Multi-objective optimization Evolutionary algorithms NSGA-II 


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  1. 1.
    Aronsson, H., Brodin, M.H.: The environmental impact of changing logistics structures. Int. J. Logist. Manag. 17(3), 394–415 (2006)CrossRefGoogle Scholar
  2. 2.
    Bauer, J., Bektas, T., Crainic, T.G.: Minimizing greenhouse gas emissions in intermodal freight transport: an application to rail service design. Journal of the Operational Research Society 61, 530–542 (2010), doi:10.1057/jors.2009.102zbMATHCrossRefGoogle Scholar
  3. 3.
    Bickel, P., Friedrich, R., Link, H., Stewart, L., Nash, C.: Introducing environmental externalities into transport pricing: Measurement and implications. Transp. Rev. 26(4), 389–415 (2006)CrossRefGoogle Scholar
  4. 4.
    Boriboonsomsin, K., Vu, A., Barth, M.: CoEco-Driving: Pilot Evaluation of Driving Behavior Changes among U.S. Drivers. University of California, Riverside (August 2010)Google Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 181–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007) ISBN 978-0-387-33254-3zbMATHGoogle Scholar
  7. 7.
    Malandraki, C., Daskin, M.S.: Time-Dependent Vehicle-Routing Problems - Formulations, Properties And Heuristic Algorithms. Transportation Science 26(3), 185–200 (1992)zbMATHCrossRefGoogle Scholar
  8. 8.
    Figliozzi, M.A.: A Route Improvement Algorithm for the Vehicle Routing Problem with Time Dependent Travel Times. In: Proceeding of the 88th Transportation Research Board Annual Meeting CD rom (2009)Google Scholar
  9. 9.
    Figliozzi, M.A.: Vehicle Routing Problem for Emissions Minimization. Transportation Research Record 2197, 1–7 (2010)CrossRefGoogle Scholar
  10. 10.
    Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, pp. 416–423. University of Illinois at Urbana- Champaign, Morgan Kaufmann Publishers (1993)Google Scholar
  11. 11.
    The Greenhouse Gas Protocol Initiative: Calculating CO2 emissions from mobile sources. Guidance to calculation worksheets (2005),
  12. 12.
    Halicioglu, F.: An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37, 1156–1164 (2009)CrossRefGoogle Scholar
  13. 13.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Piscataway, New Jersey, vol. 1, pp. 82–87. IEEE Service Center (June 1994)Google Scholar
  14. 14.
    ICF Consulting: Assessment of Greenhouse Gas Analysis Techniques for Transportation Projects. 9300 Lee Highway, Fairfax, Virginia 22031 (May 2006)Google Scholar
  15. 15.
  16. 16.
    Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  17. 17.
    David Scheffer, J., Grefenstette, J.J.: Multiobjective Learning via Genetic Algorithms. In: Proceedings of the 9th International joint Conference on Articial Intelligence(IJCAI 1985), Los Angeles, California, pp. 593–595. AAAI (1985)Google Scholar
  18. 18.
    Soylu, A., Oruc, C., Turkay, M., Fujita, K., Asakura, T.: Synergy Analysis of Collabo- rative Supply Chain Management in Energy Systems Using Multi-Period MILP. Eur. J. Oper. Res. 174(1), 387–403 (2006)zbMATHCrossRefGoogle Scholar
  19. 19.
    Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  20. 20.
    Turkay, M., Oruc, C., Fujita, K., Asakura, T.: Multi-Company Collaborative Supply Chain Management with Economical and Environmental Considerations. Comput. Chem. Eng. 28(6-7), 985–992 (2004)CrossRefGoogle Scholar
  21. 21.
    Van Woensel, T., Kerbache, L., Peremans, H., Vandaele, N.: Vehicle routing with dynamic travel times: a queueing approach. European Journal of Operational Research 186, 990–1007 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailou, P., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, EUROGEN 2001, Athens, Greece, pp. 95–100 (2001)Google Scholar
  23. 23.
  24. 24.
    De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. dissertation, University of Michigan, USA (1975)Google Scholar
  25. 25.
    Mckinnon, A., Cullinan, S., Browne, M.: Green logistics: Improving the environmental sustainability of logistics. Kogan Page, limited (2010)Google Scholar
  26. 26.
    Liefooghe, A., Jourdan, L., Talbi, E.: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. European Journal of Operational Research 209(2), 104–112 (2011)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Toth, P., Vigo, D.: The Vehicle Routing Problem. SIAM, Philadelphia (2001) ISBN 0898715792Google Scholar
  28. 28.
    Jozefowiez, N., Semet, F., Talbi, E.: Multi-objective vehicle routing problems. European Journal of Operational Research 189(2), 293–309 (2008)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.IS Department, College of Computer and Information SciencesImam Mohammad Ibn Saud UniversityRiyadhKSA
  2. 2.Larodec Laboratory, Institut Supérieur de GestionUniversity of TunisTunisTunisia

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