The urban freight distribution is highly susceptible to unexpected events that often occur during delivery, such as delays at customer locations or due to traffic conditions. Such events may lead to inferior customer service, or higher costs, areas in which intelligent real-time fleet management may prove beneficial. In this paper, the authors present such a system that incorporates methods to estimate the expected travel time of a delivery vehicle, combining AVL-based real-time and historical data, with algorithms for efficient vehicle re-routings. The system continuously monitors the delivery process, detects possible delays in real-time, and adjusts the delivery schedule accordingly by suggesting effective re-routing strategies. The authors report results from testing the system via simulation and in a case study, and illustrate the extent of delivery performance improvements that may be achieved through such an approach.
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