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This paper investigates the construction of routes for local delivery of packages. The primary objective of this research is to provide realistic models to optimize vehicle dispatching when customer locations and demands vary from day to day while maintaining driver familiarity with their service territories, hence dispatch consistency. The objective of increasing driver familiarity tends to make routes or service territories fixed. On the other hand, to serve varying demand it is advantageous to reassign vehicles/drivers and service territories each day. To balance the trade-offs between these two objectives, we developed the concepts of “cell,” “core area,” and “flex zone,” and created a two-stage vehicle routing model—strategic core area design and operational cell routing—and explicitly evaluated the effect of driver familiarity through the use of learning and forgetting curves.

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We consider the Courier Delivery Problem, a variant of the Vehicle Routing Problem with time windows in which customers appear probabilistically and their service times are uncertain. We use scenario-based stochastic programming with recourse to model the uncertainty in customers and robust optimization for the uncertainty in service times. Our proposed model generates a master plan and daily schedules by maximizing the coverage of customers and the similarity of routes in each scenario while minimizing the total time spent by the couriers and the total earliness and lateness penalty. The computational results show that our heuristic improves the similarity of routes and the lateness penalty at the expense of increased total time spent when compared to a solution by independently scheduling each day. Our experimental results also show improvements over current industry practice on two real-world data sets.