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A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem

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  • Gianpaolo Ghiani

    (Dipartimento di Ingegneria dell'Innovazione, Università del Salento, 73100 Lecce, Italy)

  • Emanuele Manni

    (Dipartimento di Ingegneria dell'Innovazione, Università del Salento, 73100 Lecce, Italy)

  • Barrett W. Thomas

    (Department of Management Sciences, University of Iowa, Iowa City, Iowa 52242)

Abstract

Advances in information technology and telecommunications, together with ever-growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper compares two methods motivated by the opportunity that the availability of data and technology gives to improve on current practice. In particular, the two solution approaches are explored in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locations. One approach, sample-scenario planning, offers the potential for higher-quality solutions, but at the expense of greater computational effort. On the other hand, anticipatory insertion offers reduced computation and increased managerial ease, but with the potential for reduced solution quality due to restrictions on solution structure. Our results show that anticipatory insertion can often match the quality of sample-scenario planning, particularly when the degree of dynamism is low.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:46:y:2012:i:3:p:374-387
    DOI: 10.1287/trsc.1110.0374
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    Citations

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    Cited by:

    1. Srour, F.J. & Agatz, N.A.H. & Oppen, J., 2014. "Strategies for Handling Temporal Uncertainty in Pickup and Delivery Problems with Time Windows," ERIM Report Series Research in Management ERS-2014-015-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Pérez Rivera, Arturo E. & Mes, Martijn R.K., 2017. "Anticipatory freight selection in intermodal long-haul round-trips," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 176-194.
    3. Alejandro Toriello & William B. Haskell & Michael Poremba, 2014. "A Dynamic Traveling Salesman Problem with Stochastic Arc Costs," Operations Research, INFORMS, vol. 62(5), pages 1107-1125, October.
    4. Srour, F.J. & Agatz, N.A.H. & Oppen, J., 2014. "The Value of Inaccurate Advance Time Window Information in a Pick-up and Delivery Problem," ERIM Report Series Research in Management ERS-2014-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Stacy A. Voccia & Ann Melissa Campbell & Barrett W. Thomas, 2019. "The Same-Day Delivery Problem for Online Purchases," Service Science, INFORMS, vol. 53(1), pages 167-184, February.
    6. Liu, Chuanju & Lin, Shaochong & Shen, Zuo-Jun Max & Zhang, Junlong, 2023. "Stochastic service network design: The value of fixed routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    7. Ferrucci, Francesco & Bock, Stefan, 2015. "A general approach for controlling vehicle en-route diversions in dynamic vehicle routing problems," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 76-87.
    8. Marlin W. Ulmer & Barrett W. Thomas & Dirk C. Mattfeld, 2019. "Preemptive depot returns for dynamic same-day delivery," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(4), pages 327-361, December.
    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. Domokos Esztergár-Kiss, 2020. "Trip Chaining Model with Classification and Optimization Parameters," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    11. Marlin W. Ulmer & Alan Erera & Martin Savelsbergh, 2022. "Dynamic service area sizing in urban delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 763-793, September.
    12. Marlin W. Ulmer & Dirk C. Mattfeld & Felix Köster, 2018. "Budgeting Time for Dynamic Vehicle Routing with Stochastic Customer Requests," Transportation Science, INFORMS, vol. 52(1), pages 20-37, January.
    13. 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.
    14. F. Jordan Srour & Niels Agatz & Johan Oppen, 2018. "Strategies for Handling Temporal Uncertainty in Pickup and Delivery Problems with Time Windows," Transportation Science, INFORMS, vol. 52(1), pages 3-19, January.
    15. Ritzinger, Ulrike & Puchinger, Jakob & Rudloff, Christian & Hartl, Richard F., 2022. "Comparison of anticipatory algorithms for a dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 301(2), pages 591-608.
    16. Marlin W. Ulmer & Leonard Heilig & Stefan Voß, 2017. "On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(3), pages 161-171, June.
    17. E. Angelelli & R. Mansini & M. Vindigni, 2016. "The Stochastic and Dynamic Traveling Purchaser Problem," Transportation Science, INFORMS, vol. 50(2), pages 642-658, May.
    18. Farzaneh Karami & Wim Vancroonenburg & Greet Vanden Berghe, 2020. "A periodic optimization approach to dynamic pickup and delivery problems with time windows," Journal of Scheduling, Springer, vol. 23(6), pages 711-731, December.
    19. Avraham, Edison & Raviv, Tal, 2021. "The steady-state mobile personnel booking problem," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 266-288.
    20. Marlin W. Ulmer & Justin C. Goodson & Dirk C. Mattfeld & Marco Hennig, 2019. "Offline–Online Approximate Dynamic Programming for Dynamic Vehicle Routing with Stochastic Requests," Service Science, INFORMS, vol. 53(1), pages 185-202, February.
    21. Martin Savelsbergh & Tom Van Woensel, 2016. "50th Anniversary Invited Article—City Logistics: Challenges and Opportunities," Transportation Science, INFORMS, vol. 50(2), pages 579-590, May.

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