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A Model and Algorithm for the Courier Delivery Problem with Uncertainty

Author

Listed:
  • Ilgaz Sungur

    (Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089)

  • Yingtao Ren

    (Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089)

  • Fernando Ordóñez

    (Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089)

  • Maged Dessouky

    (Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089)

  • Hongsheng Zhong

    (UPS, Timonium, Maryland 21093)

Abstract

We consider the courier delivery problem (CDP), a variant of the vehicle routing problem with time windows (VRPTW) in which customers appear probabilistically and their service times are uncertain. We use scenario-based stochastic programming with recourse to model the uncertainty in customers and robust optimization for the uncertainty in service times. Our proposed model generates a master plan and daily schedules by maximizing the coverage of customers and the similarity of routes in each scenario, while minimizing the total time spent by the couriers and the total earliness and lateness penalty. To solve large-scale problem instances, we develop an insertion-based solution heuristic, called master and daily scheduler (MADS), and a tabu search improvement procedure. The computational results show that our heuristic improves the similarity of routes and the lateness penalty at the expense of increased total time spent when compared to a solution of independently scheduling each day. Our experimental results also show improvements over current industry practice on two real-world data sets.

Suggested Citation

  • Ilgaz Sungur & Yingtao Ren & Fernando Ordóñez & Maged Dessouky & Hongsheng Zhong, 2010. "A Model and Algorithm for the Courier Delivery Problem with Uncertainty," Transportation Science, INFORMS, vol. 44(2), pages 193-205, May.
  • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:2:p:193-205
    DOI: 10.1287/trsc.1090.0303
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    References listed on IDEAS

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    1. Gilbert Laporte & François Louveaux & Hélène Mercure, 1992. "The Vehicle Routing Problem with Stochastic Travel Times," Transportation Science, INFORMS, vol. 26(3), pages 161-170, August.
    2. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    3. Dimitris J. Bertsimas & Patrick Jaillet & Amedeo R. Odoni, 1990. "A Priori Optimization," Operations Research, INFORMS, vol. 38(6), pages 1019-1033, December.
    4. Beasley, J. E. & Christofides, N., 1997. "Vehicle routing with a sparse feasibility graph," European Journal of Operational Research, Elsevier, vol. 98(3), pages 499-511, May.
    5. Patrick Jaillet, 1988. "A Priori Solution of a Traveling Salesman Problem in Which a Random Subset of the Customers Are Visited," Operations Research, INFORMS, vol. 36(6), pages 929-936, December.
    6. Chris Groër & Bruce Golden & Edward Wasil, 2009. "The Consistent Vehicle Routing Problem," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 630-643, February.
    7. Dimitris J. Bertsimas, 1992. "A Vehicle Routing Problem with Stochastic Demand," Operations Research, INFORMS, vol. 40(3), pages 574-585, June.
    8. M A Haughton, 2000. "Quantifying the benefits of route reoptimisation under stochastic customer demands," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(3), pages 320-332, March.
    9. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    10. D. Goldfarb & G. Iyengar, 2003. "Robust Portfolio Selection Problems," Mathematics of Operations Research, INFORMS, vol. 28(1), pages 1-38, February.
    11. Hongsheng Zhong & Randolph W. Hall & Maged Dessouky, 2007. "Territory Planning and Vehicle Dispatching with Driver Learning," Transportation Science, INFORMS, vol. 41(1), pages 74-89, February.
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