Kai Winheller

Academic Staff

Kai Winheller, M.Sc.

LC 128a
+49 203 37-91220


  • Schur, Rouven; Winheller, Kai: Optimizing Last-Mile Delivery: A Dynamic Compensation Strategy for Occasional Drivers. PDFCitationDetails

    Amid the rapid growth of online retail, last-mile delivery faces significant challenges, including the cost-effective delivery of goods to all customers. Accordingly, the development and improvement of innovative approaches thrive in current research. 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 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. Thereby, 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. Designed to cope with these challenges, our approximation methods, a parametric value function approximation and a fluid approximation, aim to accurately determine avoided costs, which are a key factor in calculating optimal compensation.

    A comprehensive simulation study compares our algorithms with benchmark strategies, and shows the advantages of dynamic compensation across a range of scenarios. We conclude our work with managerial insights and a summary of our findings, offering significant implications for last-mile delivery operations.


  • 21. Arbeitsgruppensitzung der GOR AG "Pricing & Revenue Management" (Frankfurt 28.02.-01.03.2024) 
  • OR 2023  — Annual Conference of the Pperations Research Society of Germany (Hamburg, 29.08.-01.09.2023)
  • 32. Workshop für QBWL (Bad Windsheim, 20.-24.03.2023)  — Vortrag: "Dynamic Compensations for Occasional Drivers"
  • 19. AG Sitzung der Arbeitsgruppe "Pricing & Revenue Management" (Düsseldorf, 03.03.2023)
  • OR 2022  — Annual Conference of the Operations Research Society of Germany (Karlsruhe, 06.-09.2022)
  • 31. Workshop für QBWL (Online, 28.-29.03.2022)


Python Programmierkurs, Stochastische Optimierung


Gesellschaft für Operations Research e.V. (GOR)