Metaheuristic Optimization of Multiproduct Batch Plant Design

Pharmaceutical researchers and biotechnology companies are devoted to developing medicines, such as: therapeutic proteins, human insulin, vaccines for hepatitis, food grade protein, chymosin detergent enzyme, and cryophilic protease. This allows patients to live longer, healthier, and more productive. Within this context, there is a high degree of consensus in the biomanufacturing industry that product quality, customer service, and cost ef¿ciency are fundamental for success. In order to set up a strategy for building an adequate flexibility to manufacture different multiproduct drug substances, such as designing a plant, determining the number of units for a speci¿c task, assigning raw materials to different production processes, and deciding the production planning. The aim of this work is to minimize the investment cost and ¿nd out the number and size of parallel equipment units in each stage of multiproduct batch plant design (MBPD). In this book, it is proposed the problem using metaheuristic optimization algorithms as genetic algorithms, particle swarm algorithms, gradient boosting algorithms, gradient boosting regressor with grid search support.