Advertisement

Vehicle Routing to Minimizing Hybrid Fleet Fuel Consumption

  • Fei Peng
  • Amy M. Cohn
  • Oleg Gusikhin
  • David Perner
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 579)

Abstract

In this paper, we address a variant of the Vehicle Routing Problem (VRP) where the fleet contains vehicles that not only vary in performance, but this variation is a function of the arc type, such that a given vehicle might have costs lower on some arcs but higher on others. We refer to this as the Hybrid Fleet Vehicle Routing Problem. This is more realistic than the common assumption that all vehicles are identical. In many cases, fleets are made up of different vehicle types, varying in size, engine/fuel type, and other performance-impacting factors. Even in a homogeneous fleet, vehicles often differ by age and condition, which can greatly impact performance. We propose two heuristic methods that take into account the vehicle-specific cost structures. We provide computational results to demonstrate the quality of our solutions, and a comparison with a Genetic Algorithm based method.

Keywords

Vehicle routing problem Heterogeneous feet Heuristics 

References

  1. 1.
    Gusikhin, O., MacNeille, P., Cohn, A.: Vehicle routing to minimize mixed-fleet fuel consumption and environmental impact. In: Proceedings of 7th International Conference on Informatics in Control, Automation and Robotics, vol. 1, pp. 285–291, Funchal, Madeira, Portugal (2010)Google Scholar
  2. 2.
    Lin, C., Choy, K.L., Ho, G.T., Chung, S., Lam, H.: Survey of green vehicle routing problem: past and future trends. Expert Syst. Appl. 41, 1118–1138 (2014)CrossRefGoogle Scholar
  3. 3.
    Minett, C.F., Daamen, W., Van Arem, B., Kuijpers, S., et al.: Eco-routing: comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles. In: 2011 IEEE Forum on Integrated and Sustainable Transportation System (FISTS), pp. 32–39. IEEE (2011)Google Scholar
  4. 4.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6, 80–91 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Baldacci, R., Battarra, M., Vigo, D.: Routing a heterogeneous fleet of vehicles. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges, vol. 43, pp. 3–27. Springer US, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Laporte, G.: Fifty years of vehicle routing. Transp. Sci. 43, 408–416 (2009)CrossRefGoogle Scholar
  7. 7.
    Toth, P., Vigo, D. (eds.): The Vehicle Routing Problem. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (2001)Google Scholar
  8. 8.
    Baldacci, R., Bartolini, E., Laporte, G.: Some applications of the generalized vehicle routing problem. J. Oper. Res. Soc. 61, 1072–1077 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Tahmassebi, T.: Vehicle routing problem (VRP) formulation for continuous-time packing hall design/operations. Comput. Chem. Eng. 23(suppl S), 1011–1014 (1999)CrossRefGoogle Scholar
  10. 10.
    Hasle, G., Kloster, O.: Industrial vehicle routing. In: Hasle, G., Lie, K.-A., Quak, E. (eds.) Geometric Modelling, Numerical Simulation, and Optimization, pp. 397–435. Springer, Berlin (2007)CrossRefGoogle Scholar
  11. 11.
    Clark, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12, 568–581 (1964)CrossRefGoogle Scholar
  12. 12.
    Desrochers, M., Verhoog, T.W.: A new heuristic for the fleet size and mix vehicle routing problem. Comput. Oper. Res. 18, 263–274 (1991)CrossRefzbMATHGoogle Scholar
  13. 13.
    Golden, B., Addad, A., Levy, L., Gheysens, F.: The fleet size and mix vehicle routing problem. Comput. Oper. Res. 11, 49–66 (1984)CrossRefzbMATHGoogle Scholar
  14. 14.
    Li, F., Golden, B., Wasil, E.: A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Comput. Oper. Res. 34, 2734–2742 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Salhi, S., Rand, G.K.: Incorporating vehicle routing into the vehicle fleet composition problem. Eur. J. Oper. Res. 66, 313–330 (1993)CrossRefzbMATHGoogle Scholar
  16. 16.
    Baldacci, R., Mingozzi, A.: A unified exact method for solving different classes of vehicle routing problems. Math. Program. Ser. A 120, 347–380 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Campos, V., Mota, E.: Heuristic procedures for the capacitated vehicle routing problem. Comput. Optim. Appl. 16, 265–277 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ralphs, T.K., Kopman, L., Pulleyblank, W.R., Trotter, L.E.: On the capacitated vehicle routing problem. Math. Program. 94, 343–359 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    de Oliveira, H.C.B., Vasconcelos, G.C.: A hybrid search method for the vehicle routing problem with time windows. Ann. Oper. Res. 180, 125–144 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Kim, B.I., Kim, S., Sahoo, S.: Waste collection vehicle routing problem with time windows. Comput. Oper. Res. 33, 3624–3642 (2006)CrossRefzbMATHGoogle Scholar
  21. 21.
    Kritikos, M.N., Ioannou, G.: The balanced cargo vehicle routing problem with time windows. Int. J. Prod. Econ. 123, 42–51 (2010)CrossRefGoogle Scholar
  22. 22.
    Li, X., Tian, P., Leung, S.C.: Vehicle routing problems with time windows and stochastic travel and service times: models and algorithm. Int. J. Prod. Econ. 125, 137–145 (2010)CrossRefGoogle Scholar
  23. 23.
    Novoa, C., Storer, R.: An approximate dynamic programming approach for the vehicle routing problem with stochastic demands. Eur. J. Oper. Res. 196, 509–515 (2009)CrossRefzbMATHGoogle Scholar
  24. 24.
    Choi, E., Tcha, D.W.: A column generation approach to the heterogeneous fleet vehicle routing problem. Comput. Oper. Res. 34, 2080–2095 (2007)CrossRefzbMATHGoogle Scholar
  25. 25.
    Wassan, N., Osman, I.: Tabu serach variants for the mix fleet vehicle routing problem. J. Oper. Res. Soc. 53, 768–782 (2002)CrossRefzbMATHGoogle Scholar
  26. 26.
    Taillard, É.D.: A heuristic column generation method for the heterogeneous fleet VRP. Oper. Res. - Recherche Opérationnelle 33, 1–14 (1999)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Ochi, L.S., Vianna, D.S., Drummond, L.M.A., Victor, A.O.: An evolutionary hybrid metaheuristic for solving the vehicle routing problem with heterogeneous fleet. In: Poli, R., Schoenauer, M., Fogarty, T.C., Banzhaf, W. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 187–195. Springer, Heidelberg (1998) CrossRefGoogle Scholar
  28. 28.
    Tarantilis, C., Zachariadis, E., Kiranoudis, C.: A guided tabu search for the heterogeneous vehicle routing problem. J. Oper. Res. Soc. 59, 1659–1673 (2008)CrossRefzbMATHGoogle Scholar
  29. 29.
    Armacost, A., Barnhart, C., Ware, K.: Composite variable formulations for express shipment service network design. Transp. Sci. 35, 1–20 (2002)CrossRefzbMATHGoogle Scholar
  30. 30.
    Barlatt, A., Cohn, A., Fradkin, Y., Gusikhin, O., Morford, C.: Using composite variable modeling to achieve realism and tractability in production planning: an example from automotive stamping. IIE Trans. 41, 421–436 (2009)CrossRefGoogle Scholar
  31. 31.
    Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W., Vance, P.H.: Branch-and-price: column generation for solving huge integer programs. Oper. Res. 46, 316–329 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Holland, J.: Adaptation in Natural and Artificial Systems, p. 5. University of Michigan Press, Ann Arbor (1975)Google Scholar
  33. 33.
    Baker, B., Ayechew, M.: A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30, 787–800 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Lin, S., et al.: Computer solutions of the traveling salesman problem. Bell Syst. Tech. J. 44, 2245–2269 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Kolmanovsky, I., McDonough, K., Gusikhin, O.: Estimation of fuel flow for telematics-enabled adaptive fuel and time efficient vehicle routing. In: Proceeding of 11th IEEE International Conference on Intelligent Transportation Systems Telecommunications, pp. 139–144, St. Petersburg, Russia (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Fei Peng
    • 1
  • Amy M. Cohn
    • 2
  • Oleg Gusikhin
    • 3
  • David Perner
    • 4
  1. 1.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA
  3. 3.Research and Advanced EngineeringFord Motor CompanyDearbornUSA
  4. 4.Ford Motor CompanyDearbornUSA

Personalised recommendations