Joint Inventory and Pricing Coordination with Incomplete Demand Information

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