The previous research work in the literature for capacity planning and scheduling of biopharmaceutical manufacture focused mostly on the use of mixed integer linear programming (MILP). This paper presents fast genetic algorithm (GA) approaches for solving discrete-time MILP problems of capacity planning and scheduling in the biopharmaceutical industry. The proposed approach is validated on two case studies from the literature and compared with MILP models. In case study 1, a medium-term capacity planning problem of a single-site, multi-suite, multi-product biopharmaceutical manufacture is presented. The GA is shown to achieve the global optimum on average 3.6 times faster than a MILP model. In case study 2, a larger long-term planning problem of multi-site, multi-product bio-manufacture is solved. Using the rolling horizon strategy, the GA is demonstrated to achieve near-optimal solutions (1% away from the global optimum) as fast as a MILP model.

文献中有关生物制药生产能力计划和调度的先前研究工作主要集中在混合整数线性规划（MILP）的使用上。本文提出了快速遗传算法（GA）的方法来解决生物制药行业中的产能计划和调度的离散时间MILP问题。该方法在文献中的两个案例研究中得到了验证，并与MILP模型进行了比较。在案例研究1中，提出了一个单站点，多套件，多产品生物制药生产的中期产能计划问题。事实证明，GA可以比MILP模型平均快3.6倍实现全局最优。在案例研究2中，解决了更大的多站点，多产品生物制造的长期规划问题。使用滚动视域策略，