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On the Value of Optimal Myopic Solutions for Dynamic Routing and Scheduling Problems in the Presence of User Noncompliance

Author

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  • Warren B. Powell

    (Department of Civil Engineering and Operations Research, Princeton University, Princeton, New Jersey 08544)

  • Michael T. Towns

    (Department of Civil Engineering and Operations Research, Princeton University, Princeton, New Jersey 08544)

  • Arun Marar

    (Department of Civil Engineering and Operations Research, Princeton University, Princeton, New Jersey 08544)

Abstract

The most common approach for modeling and solving routing and scheduling problems in a dynamic setting is to solve, as close to optimal as possible, a series of deterministic, myopic models. The argument is most often made that, if the data changes, then we should simply reoptimize. We use the setting of the load matching problem that arises in truckload trucking to compare the value of optimal myopic solutions versus varying degrees of greedy, suboptimal myopic solutions in the presence of three forms of uncertainty: customer demands, travel times, and, of particular interest, user noncompliance. A simulation environment is used to test different dispatching strategies under varying levels of system dynamism. An important issue we consider is that of user noncompliance, which is the effect of optimizing when users do not adopt all of the recommendations of the model. Our results show that (myopic) optimal solutions only slightly outperform greedy solutions under relatively high levels of uncertainty, and that a particular suboptimal solution actually outperforms optimal solutions under a wide range of conditions.

Suggested Citation

  • Warren B. Powell & Michael T. Towns & Arun Marar, 2000. "On the Value of Optimal Myopic Solutions for Dynamic Routing and Scheduling Problems in the Presence of User Noncompliance," Transportation Science, INFORMS, vol. 34(1), pages 67-85, February.
    • Handle: RePEc:inm:ortrsc:v:34:y:2000:i:1:p:67-85
    DOI: 10.1287/trsc.34.1.67.12283
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    2. 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.
    3. Zolfagharinia, Hossein & Haughton, Michael, 2016. "Effective truckload dispatch decision methods with incomplete advance load information," European Journal of Operational Research, Elsevier, vol. 252(1), pages 103-121.
    4. Mofidi, Seyed Shahab & Pazour, Jennifer A., 2019. "When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 1-23.
    5. Miguel Andres Figliozzi & Hani S. Mahmassani & Patrick Jaillet, 2007. "Pricing in Dynamic Vehicle Routing Problems," Transportation Science, INFORMS, vol. 41(3), pages 302-318, August.
    6. Jan Brinkmann & Marlin W. Ulmer & Dirk C. Mattfeld, 2020. "The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 69-92, April.
    7. Keskin, Merve & Branke, Juergen & Deineko, Vladimir & Strauss, Arne K., 2023. "Dynamic multi-period vehicle routing with touting," European Journal of Operational Research, Elsevier, vol. 310(1), pages 168-184.
    8. Dennis Huisman & Richard Freling & Albert P. M. Wagelmans, 2004. "A Robust Solution Approach to the Dynamic Vehicle Scheduling Problem," Transportation Science, INFORMS, vol. 38(4), pages 447-458, November.
    9. Le-Anh, Tuan & De Koster, M.B.M., 2006. "A review of design and control of automated guided vehicle systems," European Journal of Operational Research, Elsevier, vol. 171(1), pages 1-23, May.
    10. Le-Anh, T. & de Koster, M.B.M., 2004. "A Review Of Design And Control Of Automated Guided Vehicle Systems," ERIM Report Series Research in Management ERS;2004-030-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.
    11. Zolfagharinia, Hossein & Haughton, Michael A., 2017. "Operational flexibility in the truckload trucking industry," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 437-460.
    12. Zolfagharinia, Hossein & Haughton, Michael, 2018. "The importance of considering non-linear layover and delay costs for local truckers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 331-355.
    13. 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.

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