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Enhanced Dynamic Discretization Discovery for the Continuous Time Load Plan Design Problem

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  • Mike Hewitt

    (Department of Information Systems and Supply Chain Management, Quinlan School of Business, Loyola University Chicago, Chicago, Illinois 60611)

Abstract

For less-than-truckload (LTL) freight transportation carriers, as shipment sizes are small relative to trailer capacity, profitability is driven by the ability to load trailers with shipments from multiple customers. However, LTL carriers are facing increased pressure from customers to provide short delivery times, which limits their ability to hold freight, a critical tactic for consolidation. As consolidation requires the coordination of shipment paths in both space and time, shorter delivery times necessitate planning methods that can accurately estimate consolidation opportunities in time. This paper adapts and enhances the dynamic discretization discovery method designed for solving the scheduled service network design problem , defined on an arbitrarily fine discretization of time, to produce exact and heuristic methods for the LTL analog, the continuous time load plan design problem. With instances from the literature and based on data from a U.S. LTL carrier, we provide computational evidence that the algorithm can effectively solve this problem. With an analysis of high-quality solutions to the instances based on LTL carrier data, we derive insights into terminal staffing policies.

Suggested Citation

  • Mike Hewitt, 2019. "Enhanced Dynamic Discretization Discovery for the Continuous Time Load Plan Design Problem," Transportation Science, INFORMS, vol. 53(6), pages 1731-1750, November.
    • Handle: RePEc:inm:ortrsc:v:53:y:2019:i:6:p:1731-1750
    DOI: 10.1287/trsc.2019.0890
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    References listed on IDEAS

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    Cited by:

    1. Vu, Duc Minh & Hewitt, Mike & Vu, Duc D., 2022. "Solving the time dependent minimum tour duration and delivery man problems with dynamic discretization discovery," European Journal of Operational Research, Elsevier, vol. 302(3), pages 831-846.
    2. Satici, Ozgur & Dayarian, Iman, 2024. "Tactical and operational planning of express intra-city package services," Omega, Elsevier, vol. 122(C).
    3. Schwerdfeger, Stefan & Boysen, Nils, 2020. "Optimizing the changing locations of mobile parcel lockers in last-mile distribution," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1077-1094.
    4. Greening, Lacy M. & Dahan, Mathieu & Erera, Alan L., 2023. "Lead-Time-Constrained Middle-Mile Consolidation Network Design with Fixed Origins and Destinations," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    5. Belieres, Simon & Hewitt, Mike & Jozefowiez, Nicolas & Semet, Frédéric, 2021. "A time-expanded network reduction matheuristic for the logistics service network design problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    6. Yang, Senyan & Ning, Lianju & Shang, Pan & (Carol) Tong, Lu, 2020. "Augmented Lagrangian relaxation approach for logistics vehicle routing problem with mixed backhauls and time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    7. Scherr, Yannick Oskar & Hewitt, Mike & Neumann Saavedra, Bruno Albert & Mattfeld, Dirk Christian, 2020. "Dynamic discretization discovery for the service network design problem with mixed autonomous fleets," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 164-195.
    8. Hewitt, Mike & Lehuédé, Fabien, 2023. "New formulations for the Scheduled Service Network Design Problem," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 117-133.

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