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Effective inventory system management allows companies to respond quickly to the market change and develop a flexible production system to improve their competitiveness. In this paper, we study an inventory problem of a perishable product with random deterioration rate under uncertainty in both demand and supply. Such problem appears in biomass supply chains because feedstock supply is always uncertain due to weather conditions, insect population. By formulating the problem as a multiperiod stochastic inventory model, we demonstrate that its optimal inventory policy is an order‐up‐to level policy. We develop an effective algorithm combining scenario‐based optimization and Lagrangian relaxation for quickly and approximately calculating the order‐up‐to levels. A computational study shows that the algorithm can find a near‐optimal solution with a relative gap of cost less than 1% on average in comparison with the optimal inventory policy.

In this paper, we study a multi-period inventory model for a perishable product with both stochastic supply and demand in a rolling horizon framework. The product has a fixed shelf life such as fresh products, blood cells, chemicals, drugs and other pharmaceutical products. The objective is to minimize the expected total cost composed of ordering, purchasing, holding, shortage and waste costs. We focus on finding a high-quality solution close to the optimal solution of the model that provides decision support for decision-makers. We propose a stochastic programming model and transform it into MILP model based on conditional scenarios (CS) approach to reduce the computational burden. By comparing with the sample average approximation (SAA) method in a numerical study, we show that our method works efficiently.