Whenever there is sufficient demand, companies generally prefer the full truckload (TL) option for long-distance transport, resulting in large and less frequent shipment operations that can be costly if inventory carrying costs are high. Less than truckload (LTL) is another option for transport when carrying costs are high and/or there is insufficient demand. Shipment consolidation provides another option that combines many of the benefits of both TL and LTL. Shipment consolidation is a cost-effective transport solution that combines different size shipments into a single truckload. Combining many loads as a single load brings together economies of scale and potential savings. Traditional routing techniques that minimize distance are not suitable for shipments that have different origins and destinations because it can be beneficial to travel further to minimize overall transport and inventory cost, or what is termed total logistics cost (TLC). Effective consolidation of multi-stop routes to minimize TLC requires routing procedures that are more computationally intensive to find beneficial combinations of loads into consolidated shipments. In this study, we have developed a saving-based procedure to determine consolidated route sequences that minimize the TLC of shipments. Twenty-one data sets were produced using real city coordinates and population densities in North Carolina to demonstrate the effectiveness of the procedure. The solutions of the proposed method are compared with the solutions of the traditional Clarke and Wright (C-W) algorithm. Although the traditional C-W algorithm provides very fast solution times, the proposed method has produced much better solution values.


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    Title :

    Minimizing Total Logistics Cost for Long-Haul Multi-Stop Truck Transportation


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-09-09




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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