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

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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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    1. William C. Jordan & Mark A. Turnquist, 1983. "A Stochastic, Dynamic Network Model for Railroad Car Distribution," Transportation Science, INFORMS, vol. 17(2), pages 123-145, May.
    2. Dimitris J. Bertsimas & Garrett van Ryzin, 1991. "A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane," Operations Research, INFORMS, vol. 39(4), pages 601-615, August.
    3. Bertsimas, Dimitris & Van Ryzin, Garrett., 1991. "A stochastic and dynamic vehicle routing problem in the Euclidean plane," Working papers 3286-91., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Warren B. Powell, 1986. "A Stochastic Model of the Dynamic Vehicle Allocation Problem," Transportation Science, INFORMS, vol. 20(2), pages 117-129, May.
    5. Linos F. Frantzeskakis & Warren B. Powell, 1990. "A Successive Linear Approximation Procedure for Stochastic, Dynamic Vehicle Allocation Problems," Transportation Science, INFORMS, vol. 24(1), pages 40-57, February.
    6. Pierre Trudeau & Moshe Dror, 1992. "Stochastic Inventory Routing: Route Design with Stockouts and Route Failures," Transportation Science, INFORMS, vol. 26(3), pages 171-184, August.
    7. Moshe Dror & Gilbert Laporte & Pierre Trudeau, 1989. "Vehicle Routing with Stochastic Demands: Properties and Solution Frameworks," Transportation Science, INFORMS, vol. 23(3), pages 166-176, August.
    8. Powell, Warren B., 1987. "An operational planning model for the dynamic vehicle allocation problem with uncertain demands," Transportation Research Part B: Methodological, Elsevier, vol. 21(3), pages 217-232, June.
    9. Dimitris Bertsimas & Philippe Chervi & Michael Peterson, 1995. "Computational Approaches to Stochastic Vehicle Routing Problems," Transportation Science, INFORMS, vol. 29(4), pages 342-352, November.
    10. Raymond K. Cheung & Warren B. Powell, 1996. "An Algorithm for Multistage Dynamic Networks with Random Arc Capacities, with an Application to Dynamic Fleet Management," Operations Research, INFORMS, vol. 44(6), pages 951-963, December.
    11. Dimitris J. Bertsimas & David Simchi-Levi, 1996. "A New Generation of Vehicle Routing Research: Robust Algorithms, Addressing Uncertainty," Operations Research, INFORMS, vol. 44(2), pages 286-304, April.
    12. Warren B. Powell, 1996. "A Stochastic Formulation of the Dynamic Assignment Problem, with an Application to Truckload Motor Carriers," Transportation Science, INFORMS, vol. 30(3), pages 195-219, August.
    13. Stewart, William R. & Golden, Bruce L., 1983. "Stochastic vehicle routing: A comprehensive approach," European Journal of Operational Research, Elsevier, vol. 14(4), pages 371-385, December.
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    2. 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.
    3. 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.
    4. Zolfagharinia, Hossein & Haughton, Michael, 2014. "The benefit of advance load information for truckload carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 34-54.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
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    13. 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.

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