Order quantity optimization model for perishable products under continuous review (Q, r) inventory policy with stochastic demand and positive lead time

Expired perishable products impose costs on businesses, making effective inventory control essential for reducing wastage and enhancing profitability. This research proposes a mathematical model for determining the optimal order quantity of perishable products under a continuous review $\left(Q,r\right)$ inventory policy, with a fixed lifetime, stochastic demand, and a positive lead time. Unlike conventional models, the proposed approach incorporates outdating costs into the total expected cost calculation. Due to the model’s computational complexity, the evolution strategy (μ + λ) optimization algorithm is employed to optimize the order quantity ($Q^{*}$) with minimum total expected cost. Numerical experiments are carried out using a case study of a hospital blood bank in Thailand. Sensitivity analysis is conducted to examine the effect of parameter variations on $Q^{*}$. The originality of this research lies in the application of the ES (μ + λ) algorithm to efficiently optimize order quantities of perishable products under a continuous review (Q, r) inventory policy.