Optimising the Scheduling and Planning of Urban Milk Deliveries

  • Neil Urquhart
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


This paper investigates the optimisation of the delivery of dairy products to households in three urban areas. The requirement for the optimisation to be part of the existing business process has determined the approach taken. The solution is maintained in an existing customer database, with manual amendments as customers are added and deleted. The optimisation challenge is to take this solution, reduce the distance travelled, and balance the load across rounds making the minimum number of changes to the delivery network. The approach taken utilises an Evolutionary Algorithm for ordering deliveries and a multi-agent approach to reassigning deliveries between rounds. The case study suggests that distance travelled may be reduced by up to 19 %, the deviation between round lengths may be considerably reduced, with only 10 % of customers being moved between rounds.



This work was partially funded by the Scottish Funding Council Innovation Voucher scheme.


  1. 1.
    Dantzig, G., Ramser, J.: The truck dispatching problem. Manag. Sci. 6, 80–91 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Laporte, G., Toth, P.: Vehicle routing: historical perspective and recent contributions. EURO J. Transp. Logist. 2, 1–2 (2013)CrossRefGoogle Scholar
  3. 3.
    Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3, 1–16 (1995)CrossRefGoogle Scholar
  4. 4.
    Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: Heuristics for multi-attribute vehicle routing problems: a survey and synthesis. Eur. J. Oper. Res. (2013)Google Scholar
  5. 5.
    Gendreau, M., Potvin, J., Braysy, O., Lokketangen, A.: Metaheuristics for the vehicle routing problem and its extensions : a categorized bibliography. In: Golden, B., Raghaven, S., Wasil, E. (eds.) The Vehicle Routing Problem, pp. 143–169. Springer, New York (2008)Google Scholar
  6. 6.
    Cook, W.: Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation. Princeton University Press, Princeton (2012)Google Scholar
  7. 7.
    Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling salesman. Oper. Res. 21, 498–516 (1973)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Baraglia, R., Hidalgo, J.I., Perego, R.: A hybrid heuristic for the travelling salesman problem. IEEE Trans. Evol. Comput. 5, 612–622 (2001)CrossRefGoogle Scholar
  9. 9.
    Urquhart, N.: Building distribution networks using cooperative agents. In: Rennard, J. (ed.) Handbook of Research on Nature Inspired Computing for Economics and Management. Idea Group Reference, Hershey (2006)Google Scholar
  10. 10.
    Urquhart, N.B., Ross, P., Paechter, B., Chisholm, K.: Solving a real world routing problem using multiple evolutionary agents. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 871–880. Springer, Heidelberg (2002) Google Scholar
  11. 11.
    Foundation, O.: (2014).
  12. 12.
    Karich, P.: Graphhopper (2014).
  13. 13.
    Sanders, P., Schultes, D.: Highway hierarchies hasten exact shortest path queries. In: Brodal, G.S., Leonardi, S. (eds.) ESA 2005. LNCS, vol. 3669, pp. 568–579. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  14. 14.
    Runka, A., Ombuki-Berman, M.B., Ventresca, M.: A search space analysis for the waste collection vehicle routing problem with time windows. In: Genetic and Evolutionary Computation Conference, GECCO 2009, pp. 1813–1814 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of ComputingEdinburgh Napier UniversityEdinburghUK

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