This work addresses the multi-objective optimisation of manufacturing strategies of monoclonal antibodies under uncertainty. The chromatography sequencing and column sizing strategies, including resin at each chromatography step, number of columns, column diameters and bed heights, and number of cycles per batch, are optimised. The objective functions simultaneously minimise the cost of goods per gram and maximise the impurity reduction ability of the purification process. Three parameters are treated as uncertainties, including bioreactor titre, and chromatography yield and capability to remove impurities. Using chance constraint programming techniques, a multi-objective mixed integer optimisation model is proposed. Adapting both ε-constraint method and Dinkelbach's algorithm, an iterative solution approach is developed for Pareto-optimal solutions. The proposed model and approach are applied to an industrially-relevant example, demonstrating the benefits of the proposed model through Monte Carlo simulation. The sensitivity analysis of the confidence levels used in the chance constraints of the proposed model is also conducted.

这项工作解决了不确定性下单克隆抗体生产策略的多目标优化。优化了色谱测序和色谱柱大小调整策略，包括每个色谱步骤的树脂，色谱柱数，色谱柱直径和床高以及每批的循环数。目标函数同时将每克商品的成本降到最低，并使纯化过程的杂质减少能力达到最大化。将三个参数视为不确定性，包括生物反应器滴定度，色谱产率和去除杂质的能力。利用机会约束规划技术，提出了一种多目标混合整数优化模型。同时采用ε约束方法和Dinkelbach算法，针对帕累托最优解开发了一种迭代解法。所提出的模型和方法被应用于与工业相关的示例，通过蒙特卡洛仿真证明了所提出模型的好处。还对所提出的模型的机会约束中使用的置信度进行了敏感性分析。