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Modeling Distribution Problems with Time Windows. Part II: Two Customer Types

Author

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  • Carlos F. Daganzo

    (University of California, Berkeley, California)

Abstract

This paper extends the results of a previous study concerning distribution with time windows. It is recognized that customers who do not need to be allocated to a time window should receive different service than the rest. Three strategies were studied to accomplish that: stratified service, discriminating service, and staggered and discriminating service. Of these, the last strategy yields the lowest local distribution distance per customer, a distance which can be less than half that for the strategy explained in the previous paper (joint service). Stratified service, however, can yield a lower line-haul distance per customer than staggered and discriminating service. In a specific case, a numerical comparison between stratified service and staggered and discriminating service should determine the overall best choice. This choice notwithstanding, the best strategy involves overlapping vehicle tours. The conventional wisdom of dividing customers into non-overlapping clusters for vehicle routing does not seem attractive for distribution problems with time windows.

Suggested Citation

  • Carlos F. Daganzo, 1987. "Modeling Distribution Problems with Time Windows. Part II: Two Customer Types," Transportation Science, INFORMS, vol. 21(3), pages 180-187, August.
    • Handle: RePEc:inm:ortrsc:v:21:y:1987:i:3:p:180-187
    DOI: 10.1287/trsc.21.3.180
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    Cited by:

    1. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    2. Carlsson, John Gunnar & Behroozi, Mehdi, 2017. "Worst-case demand distributions in vehicle routing," European Journal of Operational Research, Elsevier, vol. 256(2), pages 462-472.
    3. Jabali, Ola & Gendreau, Michel & Laporte, Gilbert, 2012. "A continuous approximation model for the fleet composition problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1591-1606.
    4. Parisa Ahani & Amílcar Arantes & Rohollah Garmanjani & Sandra Melo, 2023. "Optimizing Vehicle Replacement in Sustainable Urban Freight Transportation Subject to Presence of Regulatory Measures," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    5. Figliozzi, Miguel Andres, 2009. "Planning approximations to the average length of vehicle routing problems with time window constraints," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 438-447, May.
    6. Robert Klein & Jochen Mackert & Michael Neugebauer & Claudius Steinhardt, 2018. "A model-based approximation of opportunity cost for dynamic pricing in attended home delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 969-996, October.
    7. Strauss, Arne & Gülpınar, Nalan & Zheng, Yijun, 2021. "Dynamic pricing of flexible time slots for attended home delivery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1022-1041.
    8. Anna Franceschetti & Ola Jabali & Gilbert Laporte, 2017. "Continuous approximation models in freight distribution management," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-433, October.
    9. Franceschetti, Anna & Honhon, Dorothée & Laporte, Gilbert & Woensel, Tom Van & Fransoo, Jan C., 2017. "Strategic fleet planning for city logistics," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 19-40.
    10. Jiwen Ge & Dorothee Honhon & Jan C. Fransoo & Lei Zhao, 2021. "Supplying to Mom and Pop: Traditional Retail Channel Selection in Megacities," Manufacturing & Service Operations Management, INFORMS, vol. 23(1), pages 19-35, 1-2.
    11. Waßmuth, Katrin & Köhler, Charlotte & Agatz, Niels & Fleischmann, Moritz, 2023. "Demand management for attended home delivery—A literature review," European Journal of Operational Research, Elsevier, vol. 311(3), pages 801-815.
    12. Yang, Xinan & Strauss, Arne K., 2017. "An approximate dynamic programming approach to attended home delivery management," European Journal of Operational Research, Elsevier, vol. 263(3), pages 935-945.
    13. van der Hagen, L. & Agatz, N.A.H. & Spliet, R. & Visser, T.R. & Kok, A.L., 2022. "Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management," ERIM Report Series Research in Management ERS-2022-001-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.
    14. Melih Çelik & Özlem Ergun & Pınar Keskinocak, 2015. "The Post-Disaster Debris Clearance Problem Under Incomplete Information," Operations Research, INFORMS, vol. 63(1), pages 65-85, February.
    15. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    16. Langevin, André & Mbaraga, Pontien & Campbell, James F., 1996. "Continuous approximation models in freight distribution: An overview," Transportation Research Part B: Methodological, Elsevier, vol. 30(3), pages 163-188, June.
    17. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.

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