Drivers are essential factors affecting the efficiency and management level of a carrier. In this thesis, the driver assignment problem is investigated and methods for obtaining lower total operational costs are introduced for small and medium-sized truck freight transportation companies. Three interrelated research topics, including the following, have been systematically studied. Firstly, extending the traditional costing and Activity-Based Costing (ABC) method, the new Time-Driven Activity-Based Costing (TDABC) method, TDABC-FTC, has been introduced for truck freight companies. Detailed implementation process flow has been designed to streamline the easy incorporation of overhead cost. Fuel costs hold about one-third of the total operational costs of truck freight transportation, and drivers’ driving behaviors heavily influence the fuel consumption rate. In this work, the On-Board Diagnostics (OBD) Ⅱ, GPS tracker and Controller Area Network (CAN) bus are used to retrieve related truck operation data and transfer these data to a central database for later processing to obtain driving behavior parameters. An artificial neural network (ANN) model, built using MATLAB toolbox, is introduced to capture the relations between driving behavior and fuel consumption rate. The fuel consumption indicators for different drivers are then developed to reflect their relative fuel consumption rate quantitatively. The driver assignment problem is modeled as an optimization problem for minimizing the total operational cost of the truck, and the NP-hard problem is solved as a mixed integer programming problem. Two solution methods, Branch and Bound, and the Hungarian algorithm, are used to solve the formulated driver assignment problem. The Hungarian algorithm has been modified to address two particular situations in the driver assignment problem. Numerical experiments are conducted to validate the effectiveness of the newly introduced TDABC model, the fuel saving oriented optimal driver assignment method associating driver behavior to truck fuel consumption rate for different transportation tasks, and the solution methods for the special optimization problems formulated in this work. The newly introduced methods were tested using real truck fleet data, showing considerable benefit of the optimal scheduling techniques, and forming the foundation for further research in this area. / Graduate

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.