Name
Abstract | ||
|---|---|---|
Less-than-truckload carriers rely on the consolidation of freight from multiple shippers to achieve economies of scale. Collected freight is routed through a number of transfer terminals at each of which shipments are grouped together for the next leg of their journeys. We study the service network design problem confronted by these carriers. This problem includes determining (1) the number of services (trailers) to operate between each pair of terminals and (2) a load plan, which specifies the sequence of transfer terminals that freight with a given origin and destination will visit. Traditionally, for every terminal and every ultimate destination, a load plan specifies a unique next terminal. We introduce the p-alt model, which generalizes traditional load plans by allowing decision makers to specify a desired number of next-terminal options for terminal-destination pairs using a vector p. We compare a number of exact and heuristic approaches for solving a two-stage stochastic variant of the p-alt model. Using this model, we show that, by explicitly considering demand uncertainty and by merely allowing up to two next-terminal options for terminal-destination pairs in the load plans, carriers can generate substantial cost savings that are comparable to the ones yielded by adopting load plans that allow for any next terminal to be a routing option for terminal-destination pairs. Moreover, by using these more flexible load plans, carriers can generate savings on the order of 10% over traditional load plan designs obtained by deterministic models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1287/trsc.2020.1009 | TRANSPORTATION SCIENCE |
Keywords | DocType | Volume |
service network design, load plan design, freight transportation, less-than-truckload, sample average approximation | Journal | 55 |
Issue | ISSN | Citations |
1 | 0041-1655 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmad Baubaid | 1 | 0 | 0.34 |
| Natashia Boland | 2 | 726 | 67.11 |
| Martin Savelsbergh | 3 | 2624 | 190.83 |