Stochastic optimization models for joint pricing and inventory replenishment of perishable products

This research is motivated by the opportunities for retailers to reduce waste and increase profitability of perishables using pricing. This paper proposes a two-stage stochastic optimization model that selects suppliers, identifies a replenishment schedule for a periodic-review inventory system with non-stationary demand and supply, and determines the timing and size of a price markdown in order to maximize retailer's profits. In this model, the first-stage problem is bilinear since it captures the additive relationship between price and demand. Therefore, we develop a solution approach which extends the Benders decomposition algorithm via a piecewise linear approximation method to solve the first-stage problem. A case study is presented to validate the model. Numerical experiments suggest that supply chain profits are enhanced by integrating inventory management with pricing decisions.