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A greedy look-ahead heuristic for the vehicle routing problem with time windows

Author

Listed:
  • G Ioannou

    (Athens University of Economics and Business)

  • M Kritikos

    (Athens University of Economics and Business)

  • G Prastacos

    (Athens University of Economics and Business)

Abstract

In this paper we consider the problem of physically distributing finished goods from a central facility to geographically dispersed customers, which pose daily demands for items produced in the facility and act as sales points for consumers. The management of the facility is responsible for satisfying all demand, and promises deliveries to the customers within fixed time intervals that represent the earliest and latest times during the day that a delivery can take place. We formulate a comprehensive mathematical model to capture all aspects of the problem, and incorporate in the model all critical practical concerns such as vehicle capacity, delivery time intervals and all relevant costs. The model, which is a case of the vehicle routing problem with time windows, is solved using a new heuristic technique. Our solution method, which is based upon Atkinson's greedy look-ahead heuristic, enhances traditional vehicle routing approaches, and provides surprisingly good performance results with respect to a set of standard test problems from the literature. The approach is used to determine the vehicle fleet size and the daily route of each vehicle in an industrial example from the food industry. This actual problem, with approximately two thousand customers, is presented and solved by our heuristic, using an interface to a Geographical Information System to determine inter-customer and depot–customer distances. The results indicate that the method is well suited for determining the required number of vehicles and the delivery schedules on a daily basis, in real life applications.

Suggested Citation

  • G Ioannou & M Kritikos & G Prastacos, 2001. "A greedy look-ahead heuristic for the vehicle routing problem with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(5), pages 523-537, May.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:5:d:10.1057_palgrave.jors.2601113
    DOI: 10.1057/palgrave.jors.2601113
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    Citations

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    Cited by:

    1. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms," Transportation Science, INFORMS, vol. 39(1), pages 104-118, February.
    2. P P Repoussis & C D Tarantilis & G Ioannou, 2007. "The open vehicle routing problem with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(3), pages 355-367, March.
    3. Marki, Fabian & Charypar, David & Axhausen, Kay, 2014. "Location choice for a continuous simulation of long periods under changing conditions," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 1-18.
    4. Braysy, Olli & Hasle, Geir & Dullaert, Wout, 2004. "A multi-start local search algorithm for the vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 159(3), pages 586-605, December.
    5. Quirion-Blais, Olivier & Chen, Lu, 2021. "A case-based reasoning approach to solve the vehicle routing problem with time windows and drivers’ experience," Omega, Elsevier, vol. 102(C).
    6. G Ioannou & M N Kritikos, 2004. "A synthesis of assignment and heuristic solutions for vehicle routing with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 2-11, January.
    7. Tan, K.C. & Chew, Y.H. & Lee, L.H., 2006. "A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 172(3), pages 855-885, August.
    8. L Tansini & O Viera, 2006. "New measures of proximity for the assignment algorithms in the MDVRPTW," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 241-249, March.
    9. Xiao, Yiyong & Konak, Abdullah, 2016. "The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 146-166.
    10. Jose Carlos Molina & Ignacio Eguia & Jesus Racero, 2019. "Reducing pollutant emissions in a waste collection vehicle routing problem using a variable neighborhood tabu search algorithm: a case study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 253-287, July.
    11. Sprenger, Ralf & Mönch, Lars, 2012. "A methodology to solve large-scale cooperative transportation planning problems," European Journal of Operational Research, Elsevier, vol. 223(3), pages 626-636.
    12. Ioannou, George & Kritikos, Manolis & Prastacos, Gregory, 2003. "A problem generator-solver heuristic for vehicle routing with soft time windows," Omega, Elsevier, vol. 31(1), pages 41-53, February.
    13. Kritikos, Manolis N. & Ioannou, George, 2010. "The balanced cargo vehicle routing problem with time windows," International Journal of Production Economics, Elsevier, vol. 123(1), pages 42-51, January.
    14. An, Qian & Gordon, Peter & Moore II, James, 2014. "Location choice for a continuous simulation of long periods under changing conditions," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 85-103.
    15. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    16. Russell Bent & Pascal Van Hentenryck, 2004. "A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 515-530, November.
    17. C D Tarantilis & G Ioannou & C T Kiranoudis & G P Prastacos, 2005. "Solving the open vehicle routeing problem via a single parameter metaheuristic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 588-596, May.
    18. P. Kabcome & T. Mouktonglang, 2015. "Vehicle Routing Problem for Multiple Product Types, Compartments, and Trips with Soft Time Windows," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2015, pages 1-9, July.
    19. Kritikos, Manolis N. & Ioannou, George, 2013. "The heterogeneous fleet vehicle routing problem with overloads and time windows," International Journal of Production Economics, Elsevier, vol. 144(1), pages 68-75.
    20. Daniela Guericke & Leena Suhl, 2017. "The home health care problem with working regulations," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 977-1010, October.
    21. Nur Azriati Mat* & Aida Mauziah Benjamin & Syariza Abdul-Rahman, 2018. "Efficiency of Heuristic Algorithms in Solving Waste Collection Vehicle Routing Problem: A Case Study," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 695-700:6.
    22. Fabian Märki & David Charypar & Kay Axhausen, 2014. "Agent-based model for continuous activity planning with an open planning horizon," Transportation, Springer, vol. 41(4), pages 905-922, July.

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