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Just-in-time logistics for far-distant suppliers: scheduling truck departures from an intermediate cross-docking terminal

Author

Listed:
  • Stefan Schwerdfeger

    (Friedrich-Schiller-Universität Jena)

  • Nils Boysen

    (Friedrich-Schiller-Universität Jena)

  • Dirk Briskorn

    (Bergische Universität Wuppertal)

Abstract

Integrating far-distant suppliers, e.g., from low-wage countries and other currency areas, into concerted just-in-time (JIT) supply concepts is a delicate planning task. One enabler, e.g., often applied in the automotive industry, is an intermediate cross-docking terminal. Such a cross-dock located in close vicinity to the targeted plant is supplied from far-distant suppliers (as JIT as the long distance allows). The ultimate JIT demands are, then, assembled in the cross-dock from intermediate storage and delivered to the close-by plant in a concerted manner. This paper treats the scheduling of the JIT deliveries from the cross-dock toward the plant. Specifically, we aim to minimize the size of the vehicle fleet required to deliver all part containers within their given JIT intervals and without violating the vehicles’ capacities. We introduce suited solution procedures and investigate managerial aspects, such as the impact of the JIT intervals, the distance of the cross-dock, and the standardization of containers on the required vehicle fleet.

Suggested Citation

  • Stefan Schwerdfeger & Nils Boysen & Dirk Briskorn, 2018. "Just-in-time logistics for far-distant suppliers: scheduling truck departures from an intermediate cross-docking terminal," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 1-21, January.
    • Handle: RePEc:spr:orspec:v:40:y:2018:i:1:d:10.1007_s00291-017-0486-y
    DOI: 10.1007/s00291-017-0486-y
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    References listed on IDEAS

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    Cited by:

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    2. Imen Hamdi & Imen Boujneh, 2022. "Particle swarm optimization based-algorithms to solve the two-machine cross-docking flow shop problem: just in time scheduling," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 947-969, September.
    3. Bingtao Quan & Sujian Li & Kuo-Jui Wu, 2022. "Optimizing the Vehicle Scheduling Problem for Just-in-Time Delivery Considering Carbon Emissions and Atmospheric Particulate Matter," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    4. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
    5. Chandra Ade Irawan & Graham Wall & Dylan Jones, 2019. "An optimisation model for scheduling the decommissioning of an offshore wind farm," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 513-548, June.
    6. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

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