In the pharmaceutical industry, the goal of a supply planner is to make efficient capacity allocation decisions that ensure an uninterrupted supply of drug products to patients and to maintain product inventory levels close to the target stock. This task can be challenging due to the limited availability of manufacturing assets, uncertainties in product demand, fluctuations in production yields, and unplanned site downtimes. It is not uncommon to observe uneven distribution of product inventories with some products carrying excess inventories, while other products may be close to a stockout. Maintaining high stock levels can have economic repercussions due to the risk of expiration of unused products (whereas products facing a stockout can adversely affect the treatment regimen of patients). The network complexity of pharmaceutical supply‐chains coupled with regulatory constraints and siloed planning systems force supply planners to rely on manual (error‐prone) decision‐making processes. Such an approach results in suboptimal capacity allocation and inventory management decisions. In this work, we propose a stochastic optimization methodology for the production scheduling of multiple drug products in lyophilization units across multiple sites. The framework leverages information obtained from historical and forecast data to generate scenarios of uncertain parameters (e.g., yield, demand, and downtimes) that can realize in the future. The optimization model determines a product filling schedule that maintains product stock levels close to targets under diverse scenarios. We show that this approach helps in avoiding reactive scheduling and in maintaining a more robust production plan than deterministic procedures (which ignore uncertainty). Specifically, planning under a stochastic optimization approach reduces the number of scenarios under which backlogs are observed and also reduces the magnitude of the backlogs.

中文翻译：

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不确定情况下药品的多站点供应计划

在制药行业中，供应计划员的目标是做出有效的产能分配决策，以确保不间断地向患者供应药品，并保持产品库存水平接近目标库存。由于制造资产的可用性有限，产品需求的不确定性，产量的波动以及计划外的站点停机时间，该任务可能具有挑战性。观察产品库存分布不均的情况并不罕见，有些产品存货过多，而其他产品则接近缺货。由于未使用产品的过期风险，保持高库存水平可能会产生经济影响（而面临缺货的产品可能会对患者的治疗方案产生不利影响）。药品供应链的网络复杂性，加上法规约束和孤立的计划系统，迫使供应计划者依赖手动（容易出错）的决策流程。这种方法导致容量分配和库存管理决策不够理想。在这项工作中，我们提出了一种随机优化方法，用于在多个地点的冻干单元中生产多种药品。该框架利用从历史和预测数据中获得的信息来生成将来可以实现的不确定参数（例如，产量，需求和停机时间）的方案。优化模型确定了产品填充计划，该计划可在各种情况下保持产品库存水平接近目标。我们证明，与确定性程序（忽略不确定性）相比，这种方法有助于避免被动式调度并保持更可靠的生产计划。具体而言，采用随机优化方法进行规划可以减少观察积压情况的数量，还可以减少积压的数量。