The trucking industry is vitally important to the economy, providing an essential service by transporting goods between businesses and consumers. The less-than-truckload (LTL) industry is an important segment, serving businesses that ship quantities between 150 lbs and 10,000 lbs. Large LTL carriers use thousands of drivers to move loads between terminals in their network, and each driver may be used for multiple dispatches between rest periods. Driver wages are a major component of transportation costs. Consequently, cost-effective driver management is of crucial importance for the profitability of LTL carriers. This thesis investigates a variety of issues related to driver management. In this thesis, we describe a dynamic driver scheduling scheme developed for a large U.S. LTL carrier. Dynamic driver scheduling is challenging because drivers must abide by a complex set of rules, including government and union regulations, and trucking moves are not pre-scheduled. The technology developed combines greedy search with enumeration of time-feasible driver duties, and is capable of generating cost-effective schedules covering 15,000 20,000 loads in minutes. One of the key tactical questions faced by an LTL carrier is how many drivers to locate at each terminal. Unionized carriers have bid drivers that can only move loads between their domicile and a designated region. The developed allocation technology determines the number of drivers to allocate to each terminal as well as the designated region for bid drivers. Computational experiments based on real-life dispatch data demonstrate the effectiveness of our domiciling methodology, and show that union rules may result in substantially larger driver fleets, in some cases up to 50% larger. Finally, we investigate a fundamental question related to driver management in order to obtain some fundamental insights: determining the minimum number of drivers required to cover a set of loaded moves. The problem is shown to be polynomially solvable without any restrictions on driver schedules. For variants with restrictions, several easily computable lower bounds are derived, integer programming formulations are presented, and fast heuristics are designed and analyzed. A computational study provides insights into the quality of the lower bounds and heuristic solutions.

This thesis focuses on decision support for regional LTL carriers. The basic operating characteristics of regional LTL carriers are similar to those of national LTL carriers, i.e., they operate linehaul networks with satellites, breakbulks, and relays to consolidate freight so as to be able to cost-effectively serve their customers. However, there are also key differences. Most importantly, because the area covered by a regional carrier is smaller, a regional carrier handles less freight (sometimes significantly less) and therefore typically has fewer consolidation opportunities, which results in higher handling and transportation costs per unit of freight. Consequently, competing with national carriers on price is difficult. Therefore, to gain or maintain market share, regional carriers have to provide better service. To be able to provide better service, regional carriers have to be more dynamic, e.g., they have to be able to deviate from their load plan when appropriate, which creates challenges for decision makers. Regional carriers deliver about 60% of their shipments within a day and almost all of their shipments within two days. Furthermore, most drivers get back to their domicile at the end of each day. Therefore, the focus of the thesis is the development of effective and efficient decision models supporting daily operations of regional LTL carriers which provide excellent service at low cost. This thesis presents an effective solution approach based on two optimization models: a dynamic load planning model and a driver assignment model. The dynamic load planning model consists of two parts: an integer program to generate the best paths for daily origin-destination freight volumes and an integer program to pack freight into trailers and trailers into loads, and to determine dispatch times for these loads. Techniques to efficiently solve these integer program solution are discussed in detail. The driver assignment model is solved in multiple stages, each stage requiring the solution of a set packing models in which columns represent driver duties. Each stages determines admissible driver duties. The quality and efficiency of the solution approach are demonstrated through a computational study with real-life data from one of the largest regional LTL carriers in the country. An important "technique" for reducing driver requirements is the use of meet-and-turn operations. A basic meet-and-turn operation involves two drivers meeting at a location in between terminals and exchange trucks. A parking lot or a rest area suffices as a meet-and-turn location. This ensures that drivers return to the terminal where they started. More sophisticated meet-and-turn operations also exist, often called drop and hook operations. In this case, drivers do not exchange trucks, but one of their trailers. The motivation in this case is not to get drivers back to their domicile, but to reduce load- miles. The thesis presents analytical results quantifying the maximum benefits of using meet and turn operations and optimization techniques for identifying profitable meet-and-turn opportunities.