Skip to main content
SpringerLink
Log in

Optimising the Scheduling and Planning of Urban Milk Deliveries

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9028))

Included in the following conference series:

Abstract

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 is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dantzig, G., Ramser, J.: The truck dispatching problem. Manag. Sci. 6, 80–91 (1959)

    Article  MATH  MathSciNet  Google Scholar 

  2. Laporte, G., Toth, P.: Vehicle routing: historical perspective and recent contributions. EURO J. Transp. Logist. 2, 1–2 (2013)

    Article  Google Scholar 

  3. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3, 1–16 (1995)

    Article  Google Scholar 

  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. 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. Cook, W.: Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation. Princeton University Press, Princeton (2012)

    Google Scholar 

  7. Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling salesman. Oper. Res. 21, 498–516 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  8. Baraglia, R., Hidalgo, J.I., Perego, R.: A hybrid heuristic for the travelling salesman problem. IEEE Trans. Evol. Comput. 5, 612–622 (2001)

    Article  Google Scholar 

  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. 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. Foundation, O.: (2014). http://www.openstreetmap.org

  12. Karich, P.: Graphhopper (2014). https://graphhopper.com/

  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)

    Chapter  Google Scholar 

  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 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Neil Urquhart

Authors
  1. Neil Urquhart
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neil Urquhart .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Urquhart, N. (2015). Optimising the Scheduling and Planning of Urban Milk Deliveries. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16549-3_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16548-6

  • Online ISBN: 978-3-319-16549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation