When sales of a product are affected by randomness in demand, etailers use dynamic pricing strategies to maximize their profits. In this article the pricing problem is formulated as a continuous-time stochastic optimal control problem, where the optimal policy can be found by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. We propose a new approach to modelling the randomness in the dynamics of sales based on diffusion processes. The model assumes a continuum approximation to the stock levels of the retailer, which should scale much better to large-inventory problems than the existing models in the revenue management literature, which are based on Poisson processes. We present closed-form solutions to the HJB equation when there is no randomness in the system. It turns out that the deterministic pricing policy is near-optimal for systems with demand uncertainty. Numerical errors in calculating the optimal pricing policy may in fact result in lower profit on average than 查看全文>>