Sponsor:

CELDi

Research Team:

Wooseung Jang, James S. Noble, Na Deng

Universities Involved:

University of Missouri

Start Date:

08/01/09

End Date:

10/31/10

Summary:

The objective of this project was to develop a mathematical model that would determine the set of one?way routes between hubs, which can be consolidated. The formulation is a case of the uncapacitated hub network design problem, which determines the optimal location of consolidation hubs and shipment policy for each origin?destination pair. Each pair has the opportunity to use direct delivery through commercial LTL service, or hub-to-hub connection representing TL service via consolidation hubs. That is, the manufacturer/retailer can locally collect items to the originating hub, send them together to the destination hub using either commercial TL service or its own fleet, and finally, distribute items to destination nodes locally. The objective function seeks to minimize the total cost of transportation, including both direct LTL shipments and consolidated TL shipments.
Road freight transportation is among the most significant and economically viable modes of transportation in the United States. The most common forms of road transportation are less-than-truckload (LTL) transportation and full truckload (FTL or TL) transportation. The objective of these transportation methods is to deliver shipments from dispersed origins to many other dispersed destinations in the most cost-efficient manner.
While there exists a large collection of commercial carriers and third-party logistics providers that plan and execute LTL services at a competitive, yet profitable rate, very few manufacturing or retail companies are able to use their own vehicle fleet and workforce to provide these freight transportation services. Typically, they cannot afford to deliver many small shipments directly to many different destinations without some methods to significantly reduce the many fixed, variable, capital and operating costs of long-haul transportation. Therefore, attempt at shipment consolidation at the company level is often infeasible.
However, there are several benefits to private consolidation, if the company has the shipment volume and transportation resources available. Most importantly, private consolidation through its own transportation network by a manufacturer/retailer with multiple factories, warehouses, and suppliers has the potential to result in significant and sustainable cost reduction. In addition, it can provide better tracking and handling of shipments and possibly reduce the delivery time. Because the commercial LTL carrier’s network often consists of both regional and local hubs that are used for transshipment and distribution operations, the process of an individual shipment from the customer traveling through an extensive network with multiple stops often results in longer travel times. Also, shipments that are loaded and unloaded several times during the course of travel are subject to the increased chance of shipment damage.
The objective of this project is to investigate hub network models that can be employed by manufacturing and retail companies to identify areas of opportunity for shipment consolidation within an existing LTL transportation network. For manufacturing/retail companies with relatively sparse LTL networks, determining areas to implement shipment consolidation is much more difficult than for the larger logistics service providers. There exists a clear need to develop models and software, which evaluate the strategic possibility of private shipment consolidation, screen a network for opportunities to implement consolidation policies, and optimize the network design to maximize the amount of transportation cost savings.
This project resulted in the optimized model and software, which design logistics networks with shipment consolidation. Specifically, we created evaluation and optimization versions of logistics network design software. The evaluation version let users compare different modes of transportation and evaluate the potential cost saving based on rough parameter estimation. On the other hand, the optimization version requires complete data to accurately maximize the overall transportation cost savings.