Transportation companies make schedules to allocate vehicles and organize appropriate driving routes in advance so that the available resources are used as efficiently as possible. During the actual operation, however, various events may occur which may result in the impracticability of the original schedule. Such unforeseen events including traffic accidents, vehicle breakdowns, and congestions are called disruptions.

In this thesis, we discuss the problem of addressing disruptions and avoiding lateness with rescheduling. The vehicle rescheduling problem (VRSP) consists of defining a new schedule for a set of previously scheduled trips with a minimum total cost. We model the VRSP in both static and dynamic manners. In dynamic VRSP, disruptions are uncertain and our solution approach is based on the techniques from stochastic programming. The models are implemented with GAMS and evaluated with random data. We focus on the comparison of the computational time and the optimal of the static and dynamic models, taking into consideration of the objective values and scheduling structures.