Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5395_Библиотеки_им_академика_М_И_Перельмана

In the following, we restrict our review to some specific scheduling frameworks proposed in the literature that have direct application toward the pharmaceuti­cal industry. Castro et al. studied a pharmaceutical batch plant and proposed a decomposition-based solution strategy to tackle the complexity of the large production facilities [33]. The same problem was further investigated by Kopanos et al., who proposed two precedence-based MILP models to address complexities related to changeover costs and due times [34]. Stefansson et al. investigated a multistage, multiproduct pharmaceutical production problem and proposed both discrete and continuous time grid-based scheduling frameworks [35]. Kabra et al. proposed a continuous time grid-based model to solve the problem introduced by Lakhdar et al. [36] and include specific features related to the handling of shelf life, waste disposal, and changeovers in a biopharmaceutical plant [37]. Liu et al. proposed a discrete time-based MIP model to simultaneously optimize produc­tion and maintenance decisions of a biopharmaceutical plant under performance decay [38]. Motivated by the real-world scheduling problem in the chemical­pharmaceutical industry, Moniz et al. proposed a discrete time-based scheduling model that solves multiple instances of the problem and produces optimal schedules and also helps to evaluate the process alternatives and their associated costs [39]. Eberle et al. proposed an immediate precedence-based scheduling model for a single-unit, single-stage, multiple product sterile drug manufacturing facility while incorporating complexities regarding sterile holding times [40].