Joint Inventory and Pricing Coordination with Incomplete Demand Information

Abstract

In retailing operations, retailers face the challenge of incomplete demand information. We develop a new concept named K-approximate convexity, which is shown to be a generalization of K-convexity, to address this challenge. This idea is applied to obtain a base-stock list-price policy for the joint inventory and pricing control problem with incomplete demand information and even non-concave revenue function. A worst-case performance bound of the policy is established. In a numerical study where demand is driven from real sales data, we find that the average gap between the profits of our proposed policy and the optimal policy is 0.27%, and the maximum gap is 4.6%.