Truckload shipments constitute a significant portion of the freight transportation industry. In recent years, truckload industry is facing a serious problem of high driver turn over rate. In this research, we present a mathematical model for multi-zone dispatching method to solve this issue. Multi-zone dispatching is a method in which a service area is divided into many zones. Truckload within a zone is carried by local drivers and the truckload between zones is carried by lane drivers. Apart from reducing the driver tour length to a desirable level, the model for multi-zone also contains some unique constraints to address some issues from the perspectives of the company and the customer. The binary integer program is solved by exact methods. As the problem size increases, exact methods fail quickly. Hence, a construction heuristic within tabu search framework is developed to solve the model. Analysis of various parameters concerned is provided to gain better insights of varied aspects of the problem. Computational results for analysis of parameters and comparison of exact and heuristic methods are provided.

Truckload shipments constitute a significant portion of the freight transportation industry. In recent years, truckload industry is facing a serious problem of high driver turn over rate. In this research, we present a mathematical model for multi-zone dispatching method to solve this issue. Multi-zone dispatching is a method in which a service area is divided into many zones. Truckload within a zone is carried by local drivers and the truckload between zones is carried by lane drivers. Apart from reducing the driver tour length to a desirable level, the model for multi-zone also contains some unique constraints to address some issues from the perspectives of the company and the customer. The binary integer program is solved by exact methods. As the problem size increases, exact methods fail quickly. Hence, a construction heuristic within tabu search framework is developed to solve the model. Analysis of various parameters concerned is provided to gain better insights of varied aspects of the problem. Computational results for analysis of parameters and comparison of exact and heuristic methods are provided.

In this thesis, we study time-sensitive applications where it is important to minimize the completion time, i.e., time passing between receiving the instance and finishing the implementation of the solution. Different from the traditional approach, we are directly focusing on the minimization of the computation time as well as finding the optimal solution to the problem. The conventional approach to these conflicting objectives is generally to trade off one for the other. As an alternative, we propose a new approach called Computation-Implementation Parallelization (CIP), and develop methods to embed the computation time into the solution-implementation to minimize the total completion time. We implement our CIP approach and show its effectiveness on a type of TSP we call the TSP Race problem, where the goal is to minimize the time between receiving the instance and finishing the travel. We demonstrate a method for determining a priori when CIP will be effective. We also implement our CIP approach on Computation-Time Limited Capacitated Vehicle Routing (CTL-CVRP) problems, and show that it is possible to decrease the computation-only time while maintaining the solution quality. By this means, some of the computation time can be set free and used to improve the customer service either by delaying the order cutoff time or dispatching the trucks earlier. As a tangential study, we develop a new TSP tour length estimation model. Our model is distribution-free, and is shown to produce very accurate estimates on many different node dispersions.