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The A Priori Dynamic Traveling Salesman Problem with Time Windows

Author

Listed:
  • Allan Larsen

    (IMM, Informatics and Mathematical Modeling, Technical University of Denmark, Richard Petersens Plads, Building 321, DK 2800 Kongens Lyngby, Denmark)

  • Oli B. G. Madsen

    (CTT, Center for Traffic and Transport, Technical University of Denmark, Bygningstorvet, Building 115, DK 2800 Kongens Lyngby, Denmark)

  • Marius M. Solomon

    (Department of Management Sciences, College of Business Administration, Northeastern University, 314 Hayden Hall, 360 Huntington Avenue, Boston, Massachusetts 02115)

Abstract

In this paper we examine the traveling saleman problem with time windows for various degrees of dynamism. In contrast to the static problem, where the dispatcher can plan ahead, in the dynamic version, part or all of the necessary information becomes available only during the day of operation. We seek to minimize lateness and examine the impact of this criterion choice on the distance traveled. Our focus on lateness is motivated by the problem faced by overnight mail service providers. We propose a real-time solution method that requires the vehicle, when idle, to wait at the current customer location until it can service another customer without being early. In addition, we develop several enhanced versions of this method that may reposition the vehicle at a location different from that of the current customer based on a priori information on future requests. The results we obtained on both randomly generated data and on a real-world case study indicate that all policies proved capable of significantly reducing lateness. Our results also show that this can be accomplished with only small distance increases. The basic policy outperformed the other methods primarily when lateness and distance were equally minimized and proved very robust in all environments studied. When only lateness was considered, the policy to reposition the vehicle at a location near the current customer generally provided the largest reductions in average lateness and the number of late customers. It also produced the least extra distance to be traveled among the relocation policies.

Suggested Citation

  • Allan Larsen & Oli B. G. Madsen & Marius M. Solomon, 2004. "The A Priori Dynamic Traveling Salesman Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 459-472, November.
  • Handle: RePEc:inm:ortrsc:v:38:y:2004:i:4:p:459-472
    DOI: 10.1287/trsc.1030.0070
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    References listed on IDEAS

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    Cited by:

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    2. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    3. Ma, Tai-Yu & Rasulkhani, Saeid & Chow, Joseph Y.J. & Klein, Sylvain, 2019. "A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 417-442.
    4. Zang, Xiaoning & Jiang, Li & Liang, Changyong & Fang, Xiang, 2023. "Coordinated home and locker deliveries: An exact approach for the urban delivery problem with conflicting time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Gianpaolo Ghiani & Emanuele Manni & Barrett W. Thomas, 2012. "A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem," Transportation Science, INFORMS, vol. 46(3), pages 374-387, August.
    6. S. F. Ghannadpour & S. Noori & R. Tavakkoli-Moghaddam, 2014. "A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority," Journal of Combinatorial Optimization, Springer, vol. 28(2), pages 414-446, August.
    7. Zhi-Long Chen & Hang Xu, 2006. "Dynamic Column Generation for Dynamic Vehicle Routing with Time Windows," Transportation Science, INFORMS, vol. 40(1), pages 74-88, February.
    8. Barrett W. Thomas, 2007. "Waiting Strategies for Anticipating Service Requests from Known Customer Locations," Transportation Science, INFORMS, vol. 41(3), pages 319-331, August.
    9. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    10. Ulmer, Marlin W. & Soeffker, Ninja & Mattfeld, Dirk C., 2018. "Value function approximation for dynamic multi-period vehicle routing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 883-899.
    11. Li, Jing-Quan & Mirchandani, Pitu B. & Borenstein, Denis, 2009. "Real-time vehicle rerouting problems with time windows," European Journal of Operational Research, Elsevier, vol. 194(3), pages 711-727, May.
    12. Yu, Xinlian & Gao, Song & Hu, Xianbiao & Park, Hyoshin, 2019. "A Markov decision process approach to vacant taxi routing with e-hailing," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 114-134.
    13. Gharehgozli, Amir & Zaerpour, Nima, 2018. "Stacking outbound barge containers in an automated deep-sea terminal," European Journal of Operational Research, Elsevier, vol. 267(3), pages 977-995.
    14. van de Klundert, J. & Wormer, L., 2008. "ASAP: the after salesman problem," Research Memorandum 054, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    15. Regnier-Coudert, Olivier & McCall, John & Ayodele, Mayowa & Anderson, Steven, 2016. "Truck and trailer scheduling in a real world, dynamic and heterogeneous context," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 389-408.

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