The Vehicle Routing Problem (VRP) has been widely studied. However, very little research can be found that includes driver considerations. Many professional trades, such as plumbers, electricians, or concrete cutters, carry specialized certifications. Unlike traditional VRP, where the driver is excluded from all considerations, not every driver technician can work on every job. To further complicate this real-world, applied study, work locations are stochastic. This study explores the effect of VRP when considering both the driver technician's certifications combined with adaptive, stochastic routes. The routing implemented first creates VRP neighborhoods using driver technician's certifications, then uses a Branch and Bound routing algorithm to create the routing solutions. A quantitative two-week pretest-posttest analysis is performed. Three key datasets were collected: Route creation time, the company's average daily travel time (in minutes), and the company's average daily fuel consumption (in gallons). Organizational Learning (OL) framework was utilized to guide the study to better understand how companies can learn as their processes become more automated. It was concluded that companies could experience OL as they vocalize and quantify their existing methods. The study further shows a significant improvement for small service companies in efficiency over the de facto standard of using human dispatchers to create the routes. All three metrics proved to be significant, with route creation time yielding an efficiency increase of 86%, fuel consumption a decrease of 30.3%, and an overall travel time decrease of 28.7%. The cost savings documented could provide a substantial competitive advantage in the marketplace and help lower vehicular greenhouse gas emissions. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]