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Optimizing departure times in vehicle routes

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  • Kok, A.L.
  • Hans, E.W.
  • Schutten, J.M.J.

Abstract

Most solution methods for the vehicle routing problem with time windows (VRPTW) develop routes from the earliest feasible departure time. In practice, however, temporary traffic congestion make such solutions non-optimal with respect to minimizing the total duty time. Furthermore, the VRPTW does not account for driving hours regulations, which restrict the available travel time for truck drivers. To deal with these problems, we consider the vehicle departure time optimization (VDO) problem as a post-processing of a VRPTW. We propose an ILP formulation that minimizes the total duty time. The results of a case study indicate that duty time reductions of 15% can be achieved. Furthermore, computational experiments on VRPTW benchmarks indicate that ignoring traffic congestion or driving hours regulations leads to practically infeasible solutions. Therefore, new vehicle routing methods should be developed that account for these common restrictions. We propose an integrated approach based on classical insertion heuristics.

Suggested Citation

  • Kok, A.L. & Hans, E.W. & Schutten, J.M.J., 2011. "Optimizing departure times in vehicle routes," European Journal of Operational Research, Elsevier, vol. 210(3), pages 579-587, May.
    • Handle: RePEc:eee:ejores:v:210:y:2011:i:3:p:579-587
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    Cited by:

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    2. Bernhardt, A. & Melo, Teresa & Bousonville, Thomas & Kopfer, Herbert, 2016. "Scheduling of driver activities with multiple soft time windows considering European regulations on rest periods and breaks," Technical Reports on Logistics of the Saarland Business School 12, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    3. Pilz, Danny & Schwerdfeger, Stefan & Boysen, Nils, 2022. "Make or break: Coordinated assignment of parking space for breaks and rest periods in long-haul trucking," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 45-64.
    4. Gilbert Laporte, 2016. "Scheduling issues in vehicle routing," Annals of Operations Research, Springer, vol. 236(2), pages 463-474, January.
    5. Said Dabia & Stefan Ropke & Tom van Woensel & Ton De Kok, 2013. "Branch and Price for the Time-Dependent Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 47(3), pages 380-396, August.
    6. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "A comparison of three idling options in long-haul truck scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 631-647.
    7. Sartori, Carlo S. & Smet, Pieter & Vanden Berghe, Greet, 2022. "Scheduling truck drivers with interdependent routes under European Union regulations," European Journal of Operational Research, Elsevier, vol. 298(1), pages 76-88.
    8. Perugia, Alessandro & Moccia, Luigi & Cordeau, Jean-François & Laporte, Gilbert, 2011. "Designing a home-to-work bus service in a metropolitan area," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1710-1726.
    9. Asvin Goel & Thibaut Vidal & Adrianus Leendert Kok, 2021. "To team up or not: single versus team driving in European road freight transport," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 879-913, December.
    10. Lecluyse, Christophe & Sörensen, Kenneth & Peremans, Herbert, 2013. "A network-consistent time-dependent travel time layer for routing optimization problems," European Journal of Operational Research, Elsevier, vol. 226(3), pages 395-413.
    11. Gilbert Laporte, 2016. "Scheduling issues in vehicle routing," Annals of Operations Research, Springer, vol. 236(2), pages 463-474, January.
    12. Fleming, Christopher L. & Griffis, Stanley E. & Bell, John E., 2013. "The effects of triangle inequality on the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 224(1), pages 1-7.
    13. Thomas R. Visser & Remy Spliet, 2020. "Efficient Move Evaluations for Time-Dependent Vehicle Routing Problems," Transportation Science, INFORMS, vol. 54(4), pages 1091-1112, July.
    14. Vital, Filipe & Ioannou, Petros, 2022. "Optimizing Fuel Consumption and Pollutant Emissions in Truck Routing with Parking Availability Prediction and Working Hours Constraints," Institute of Transportation Studies, Working Paper Series qt8rw99523, Institute of Transportation Studies, UC Davis.
    15. Marie-Eve Rancourt & Jean-François Cordeau & Gilbert Laporte, 2013. "Long-Haul Vehicle Routing and Scheduling with Working Hour Rules," Transportation Science, INFORMS, vol. 47(1), pages 81-107, February.
    16. Wang, Xuping & Ruan, Junhu & Shi, Yan, 2012. "A recovery model for combinational disruptions in logistics delivery: Considering the real-world participators," International Journal of Production Economics, Elsevier, vol. 140(1), pages 508-520.
    17. Maximilian Schiffer & Michael Schneider & Grit Walther & Gilbert Laporte, 2019. "Vehicle Routing and Location Routing with Intermediate Stops: A Review," Transportation Science, INFORMS, vol. 53(2), pages 319-343, March.
    18. Vital, Filipe & Ioannou, Petros, 2021. "Scheduling and shortest path for trucks with working hours and parking availability constraints," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 1-37.
    19. Pottel, Steffen & Goel, Asvin, 2022. "Scheduling activities with time-dependent durations and resource consumptions," European Journal of Operational Research, Elsevier, vol. 301(2), pages 445-457.

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