Markets With Memory: Dynamic Channel Optimization Models With Price-Dependent Stochastic Demand

Abstract

Almost every vendor faces uncertain and time-varying demand. Inventory level and price optimization while catering to stochastic demand are conventionally formulated as variants of newsvendor problem. Despite its ubiquity in potential applications, the time-dependent (multi-period) newsvendor problem in its general form has received limited attention in the literature due to its complexity and the highly nested structure of its ensuing optimization problems. The complexity level rises even more when there are more than one decision maker in a supply channel, trying to reach an equilibrium. The purpose of this paper is to construct an explicit and e cient solution procedure for multi-period price-setting newsvendor problems in a Stackelberg framework. In particular, we show that our recursive solution algorithm can be applied to standard contracts such as buy back contracts, revenue sharing contracts, and their generalizations.