A less-than-truckload (LTL) carrier typically delivers shipments less than 10,000 pounds (classified as LTL shipment). The size of the shipment in LTL networks provides ample opportunities for consolidation. LTL carriers have focused on hub-and-spoke based consolidation to realize economies of scale. Generally, hub-and-spoke systems work as follows: the shipment is picked up from the shipper and brought to an origin terminal, which is the entry point into the hub-and-spoke system. From the terminal, the freight is sent to the first hub, where it is sorted and consolidated with other shipments, and then sent on to a second hub. It is finally sent from the second hub to the destination terminal, which is the exit point of the hub-and-spoke system.
However, the flow of shipments is often more complicated in practice. In an attempt to reduce sorting costs, load planners sometimes take this hub-and-spoke infrastructure and modify it considerably to maximize their truck utilization while satisfying service constraints. Decisions made by a load planner may have a cascading effect on load building throughout the network. As a result, decentralized load planning may result in expensive global solutions.
Academic as well as industrial researchers have adapted a hierarchical approach to design the hub-and-spoke networks: generate the hub-and-spoke network, route shipments within this hub-and-spoke network (generate a load plan) and finally, balance the empty trailers. We present mathematical models and heuristics for each of the steps involved in the design of the hub-and-spoke network. The heuristics are implemented in a user-friendly graphical tool that can help understand patterns of freight flow and provide insights into the design of the hub-and-spoke network. We also solved the load planning sub-problem in a parallel computation environment to achieve significant speed-ups. Because of the quick solution times, the tool lays the foundation to address pressing further research questions such as deciding location and number of hubs.
We have used data provided by Roadway Parcel Services, Inc. (RPS), now FedEx Ground, as a case-study for the heuristics. Our solutions rival the existing industry solutions which have been a product of expensive commercial software and knowledge acquired by the network designers in the industry.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5233 |
| Date | 12 April 2004 |
| Creators | Dave, Devang Bhalchandra |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 2269520 bytes, application/pdf |
Page generated in 0.0022 seconds