Abstract

Fill-and-finish is among the most commonly outsourced operations in biopharmaceutical manufacturing and involves several challenges. For example, fill-operations have a random production yield, as biopharmaceutical drugs might lose their quality or stability during these operations. In addition, biopharmaceuticals are fragile molecules that need specialized equipment with limited capacity, and the associated production quantities are often strictly regulated. The non-stationary nature of the biopharmaceutical demand and limitations in forecasts add another layer of challenge in production planning. Furthermore, most companies tend to “freeze” their production decisions for a limited period of time, in which they do not react to changes in the manufacturing system. Using such freeze periods helps to improve stability in planning, but comes at a price of reduced flexibility. To address these challenges, we develop a finite-horizon, discounted-cost Markov decision model, and optimize the production decisions in biopharmaceutical fill-and-finish operations. We characterize the structural properties of optimal cost and policies, and propose a new, zone-based decision-making approach for these operations. More specifically, we show that the state space can be partitioned into decision zones that provide guidelines for optimal production policies. We illustrate the use of the model with an industry case study.

摘要

填充和完成是生物制药生产中最常见的外包业务之一，涉及许多挑战。例如，由于生物制药药物在这些操作过程中可能会失去质量或稳定性，因此灌装操作的产量是随机的。另外，生物药物是易碎的分子，需要容量有限的专用设备，并且通常严格控制相关的生产量。生物药物需求的非平稳性质和预测的局限性在生产计划中增加了另一层挑战。此外，大多数公司倾向于在有限的时间段内“冻结”其生产决策，在此期间，他们对制造系统的变化不做出反应。使用这样的冻结期有助于提高计划的稳定性，但代价是灵活性降低。为了解决这些挑战，我们开发了有限水平，折价的马尔可夫决策模型，并优化了生物制药灌装和加工操作中的生产决策。我们描述了最佳成本和政策的结构特征，并针对这些运营提出了一种新的基于区域的决策方法。更具体地说，我们表明状态空间可以划分为决策区域，为最佳生产策略提供指导。我们通过行业案例研究说明了该模型的使用。我们描述了最佳成本和政策的结构特征，并针对这些运营提出了一种新的基于区域的决策方法。更具体地说，我们表明状态空间可以划分为决策区域，为最佳生产策略提供指导。我们通过行业案例研究说明了该模型的使用。我们描述了最佳成本和政策的结构特征，并针对这些运营提出了一种新的基于区域的决策方法。更具体地说，我们表明状态空间可以划分为决策区域，为最佳生产策略提供指导。我们通过行业案例研究说明了该模型的使用。