In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by applications in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. In these applications, since individual product demands are linked through consumer choice processes, the seller must

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... rmulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For a general model of consumer choice, we model this multi-product dynamic pricing problem as a stochastic dynamic program and characterize its optimal prices. In addition, since consumer behavior depends on the nature of product differentiation, we specialize the general choice model to capture vertical and horizontal differentiation. When products are vertically differentiated, our results show monotonicity properties of the optimal prices and reveal that the optimal prices can be determined by considering aggregate inventories of products rather than their individual inventory levels. Accordingly, we develop a polynomial-time exact algorithm for determining the optimal prices. When products are horizontally differentiated, we find that analogous structural properties do not hold and the behavior of optimal prices is substantially different. To solve this problem, we develop a variant of the backward induction algorithm that uses cubic spline interpolation to construct the value functions at each stage. We demonstrate that this interpolation-based algorithm has low memory requirements and generates near-optimal solutions that result in an average error of less than 0.15%.