Optimizing last-mile delivery: a dynamic compensation strategy for occasional drivers

Amid the rapid growth of online retail, last-mile delivery faces significant challenges, including the cost-effective delivery of goods to all delivery locations. Our work contributes to this stream by applying dynamic pricing techniques to effectively model the possible involvement of the crowd in fulfilling delivery tasks. The use of occasional drivers (ODs) as a viable, cost-effective alternative to traditional dedicated drivers (DDs) prompts the necessity to focus on the inherent challenge posed by the uncertainty of ODs’ arrival times and willingness to perform deliveries. We introduce a dynamic programming framework that offers individualized bundles of a delivery task and compensation to ODs as they arrive. This model, akin to a reversed form of dynamic pricing, accounts for ODs’ decision-making by treating their acceptance thresholds as a random variable. Therefore, our model addresses the dynamic and stochastic nature of OD availability and decision-making. We analytically solve the stage-wise optimization problem, outline inherent challenges such as the curses of dimensionality, and present structural properties. Tailored to meet these challenges, our approximation methods aim to accurately determine avoided costs, which are a key factor in calculating optimal compensation. Our simulation study reveals that the savings generated by involving ODs in deliveries can be significantly increased through our individualized dynamic compensation policy. This approach not only excels in generating savings for the firm but also provides a utility surplus for ODs. Additionally, we demonstrate the applicability of our approach to scenarios with time windows and illustrate the trade-off that arises from time window partitioning.