A dynamic pharmaceutical inventory investment management model during pandemics using metaheuristic algorithms

This study presents a comprehensive approach to managing pharmaceutical inventory during pandemics. The study focuses on optimizing investment strategies for promoting COVID-19 medicine across various price ranges while carefully preserving pharmaceutical products. We develop a customized inventory model that accounts for item degradation, considering factors such as price, infection rate, and preservation methods. This model is adaptable to three pandemic scenarios, with the deterioration rate influenced by the level of investment in preservation technology. Our approach employs optimal control theory to dynamically adjust investment rates, maximizing the effectiveness of resource allocation. We also utilize advanced optimization algorithms, including Ant Colony and Cuckoo Search Algorithms, to optimize pricing, preservation strategies, and replenishment schedules. Through numerical experiments, we demonstrate the efficacy of our dynamic investment approach, providing empirical evidence of its effectiveness. Additionally, sensitivity analysis on key parameters offers valuable insights for decision-makers, highlighting the importance of dynamically managing pharmaceutical inventory during pandemics. Our study provides practical solutions and managerial insights for informed pharmaceutical inventory decisions during the pandemic.