Flexibility in Biopharmaceutical Manufacturing Using Particle Swarm Algorithms and Genetic Algorithms

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 efficiency are fundamental for success. Based on our knowledge there has not been an adequate flexibility strategy to manufacture different multiproduct drug substances, such as designing a plant, determining the number of units for a specific 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 find out the number and size of parallel equipment units in each stage of multiproduct batch plant design (MBPD). For this purpose, it is proposed to solve the problem in two different ways: the first way is by using a particle swarm algorithm (PSA) and the second way is by a genetic algorithm (GA). This paper presents the effectiveness and performance comparison of PSA and GA for optimal design of a MBPD. The experimental results (given by investment cost, number and size of equipment, computational time, and idle times within the plant) obtained by the GA are better than those found by the PSA. This methodology can help the decision makers, and constitutes a very promising framework for finding a set of good solutions.