Courier delivery services deal with the problem of routing a fleet of vehicles from a depot to service a set of customers that are geographically dispersed. In many cases, in addition to a regular uncertain demand, the industry is faced with sporadic, tightly constrained, urgent requests. An example of such an application is the transportation of medical specimens, where timely, efficient, and accurate delivery is crucial in providing high quality and affordable patient services.
In the first part of this study, we propose to develop better vehicle routing solutions that can efficiently satisfy random demand over time and rapidly adjust to satisfy these sporadic, tightly constrained, urgent requests. We formulate a multi-trip vehicle routing problem using mixed integer programming. We use stochastic programming with recourse for daily plans to address the uncertainty in customer occurrence. The recourse action considers a multi-objective function that maximizes demand coverage, maximizes the quality of delivery service, and minimizes travel cost. Because of the computational difficulty for large size problems, we devise an insertion based heuristic in the first phase, and then use Tabu Search to find an efficient solution to the problem. Simulations have been done on randomly generated data and on a real data set provided by a leading healthcare provider in Southern California. Our approach has shown significant improvement in travel costs as well as in quality of service as measured by route similarity than existing methods.
In the second part of this thesis, we study a method to cluster the customers into groups without generating the actual routes. There is uncertainty in customer occurrence in the problem studied; we therefore develop a new objective function based on expected distance that includes the occurring probability of customers. We show the appropriateness of the new objective, and propose a mixed integer programming formulation for customer clustering based on it. A Tabu search based heuristic approach is developed for solving the problem, and we conduct experiments on randomly generated data. The new approach shows improvement in expected distance of routes over a clustering method based on building routes for instances where the depot is in a corner.