New Approach for Routing Carrier Delivery Services with Urgent Demand

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 application is the transportation of medical specimens, where timely, efficient, and accurate delivery is crucial in providing high quality and affordable patient services. In this work 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 devise an insertion based heuristic in the first phase, and use stochastic programming with recourse for daily plans to address the uncertainty in customer occurrence. The resource action for daily plans, 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, Tabu Search has been used 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.