In this paper, a simulation-optimization approach based on the stochastic counterpart or sample path method is used for optimizing tactical and operative decisions in the pharmaceutical supply chain. This approach focuses on the pharmacy-hospital echelon, and it takes into account random elements related to demand, prices and the lead times of medicines. Based on this approach, two mixed integer programming (MIP) models are formulated, these models correspond to the stochastic counterpart approximating problems. The first model considers expiration dates, the service level required, perishability, aged-based inventory levels and emergency purchases; the optimal policy support decisions related to the replenishment, supplier selection and the inventory management of medicines. The results of this model have been evaluated over real data and simulated scenarios. The findings show that the optimal policy can reduce the current hospital supply and managing costs in medicine planning by 16% considering 22 types of medicines. The second model is a bi-objective optimization model solved with the epsilon-constraint method. This model determines the maximum acceptable expiration date, thereby minimizing the total amount of expired medicines.

本文采用一种基于随机对应物或样本路径方法的仿真优化方法来优化药品供应链中的战术和运营决策。这种方法侧重于药房-医院梯队，并考虑了与需求，价格和药品交货时间有关的随机因素。基于此方法，制定了两个混合整数规划（MIP）模型，这些模型对应于随机对应物逼近问题。第一个模型考虑了失效日期，所需的服务水平，易腐性，基于老化的库存水平和紧急采购；与药品的补充，供应商选择和库存管理有关的最佳政策支持决策。该模型的结果已通过实际数据和模拟方案进行了评估。研究结果表明，考虑到22种药物，最优政策可以将当前医院的药物供应和药物规划管理成本降低16％。第二个模型是用epsilon约束方法求解的双目标优化模型。该模型确定了最大可接受的失效日期，从而使过期药物的总量最小化。