| Résumé: | We consider the revenue management problem of
finding profit-maximising prices for delivery time slots in the
context of attended home delivery. This multi-stage optimal
control problem admits a dynamic programming formulation
that is intractable for realistic problem sizes due to the socalled “curse of dimensionality”. Therefore, we study three
approximate dynamic programming algorithms both from a
control-theoretical perspective and numerically. Our analysis is
based on real-world data, from which we generate multiple
scenarios to stress-test the robustness of the pricing policies to
errors in model parameter estimates. Our theoretical analysis and
numerical benchmark tests indicate that one of these algorithms,
namely gradient-bounded dynamic programming, dominates the
others with respect to computation time and profit-generation
capabilities of the pricing policies that it generates.
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