A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management

TLDR

A firm with component inventories produces multiple products over a finite horizon, where each product’s stochastic demand depends on the vector of prices and the time of offer. The goal is to set product prices to maximize total expected revenue over the finite sales horizon. The authors analyze a deterministic version of the problem, derive two asymptotically optimal heuristics for the stochastic case, and illustrate their use in network yield‑management applications. They establish an upper bound on optimal revenue, show the heuristics are asymptotically optimal, and demonstrate through numerical examples that the approach effectively models diverse problems and yields fundamental insights into yield‑management performance.

Abstract

A firm has inventories of a set of components that are used to produce a set of products. There is a finite horizon over which the firm can sell its products. Demand for each product is a stochastic point process with an intensity that is a function of the vector of prices for the products and the time at which these prices are offered. The problem is to price the finished products so as to maximize total expected revenue over the finite sales horizon. An upper bound on the optimal expected revenue is established by analyzing a deterministic version of the problem. The solution to the deterministic problem suggests two heuristics for the stochastic problem that are shown to be asymptotically optimal as the expected sales volume tends to infinity. Several applications of the model to network yield management are given. Numerical examples illustrate both the range of problems that can be modeled under this framework and the effectiveness of the proposed heuristics. The results provide several fundamental insights into the performance of yield management systems.