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A multiperiod auto-carrier transportation problem with probabilistic future demands

Author

Listed:
  • Christian Billing

    (University of Augsburg, Sustainable Operations and Logistics)

  • Florian Jaehn

    (Helmut Schmidt University-University of the Federal Armed Forces Hamburg, Management Science and Operations Research)

  • Thomas Wensing

    (INFORM GmbH)

Abstract

In this paper we study the problem of delivering finished vehicles from a logistics yard to dealer locations at which they are sold. The requests for cars arrive dynamically and are not announced in advance to the logistics provider who is granted a certain time-span until which a delivery has to be fulfilled. In a real-world setting, the underlying network is relatively stable in time, since it is usually a rare event that a new dealership opens or an existing one terminates its service. Therefore, probabilities for incoming requests can be derived from historical data. The study explores the potential of using such probabilities to improve the day-to-day decision of sending out or postponing cars that are ready for delivery. Apart from the order selection, we elaborate a heuristic to optimize delivery routes for the selected vehicles, whereby special loading constraints are considered to meet the particular constraints of car transportation via road. In a case study, we illustrate the value of introducing probabilistic information to the planning process and compare the quality of different configurations of our approach.

Suggested Citation

  • Christian Billing & Florian Jaehn & Thomas Wensing, 2018. "A multiperiod auto-carrier transportation problem with probabilistic future demands," Journal of Business Economics, Springer, vol. 88(7), pages 1009-1028, September.
    • Handle: RePEc:spr:jbecon:v:88:y:2018:i:7:d:10.1007_s11573-018-0904-x
    DOI: 10.1007/s11573-018-0904-x
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Chen, Feng & Wang, Yu, 2020. "Downward compatible loading optimization with inter-set cost in automobile outbound logistics," European Journal of Operational Research, Elsevier, vol. 287(1), pages 106-118.
    2. 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.
    3. Sun, Yanshuo & Kirtonia, Sajeeb & Chen, Zhi-Long, 2021. "A survey of finished vehicle distribution and related problems from an optimization perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Soeffker, Ninja & Ulmer, Marlin W. & Mattfeld, Dirk C., 2022. "Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review," European Journal of Operational Research, Elsevier, vol. 298(3), pages 801-820.
    5. Nikola Mardešić & Tomislav Erdelić & Tonči Carić & Marko Đurasević, 2023. "Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment," Mathematics, MDPI, vol. 12(1), pages 1-44, December.

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    More about this item

    Keywords

    Automotive industry; Vehicle routing problem; Case study;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L62 - Industrial Organization - - Industry Studies: Manufacturing - - - Automobiles; Other Transportation Equipment; Related Parts and Equipment
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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