Optimizing pharmaceutical inventory and investment strategies during pandemics: A dynamic approach integrating environmental emission rates and advanced optimization algorithms

This study presents a strategy for managing pharmaceutical inventory during pandemics, focusing on optimizing investment in COVID-19 medicines while ensuring product preservation. A customized inventory model considers critical factors such as price, infection rate, and preservation, adaptable to various pandemic scenarios. Optimal control theory is applied for dynamic investment adjustments, enhancing resource allocations and decision-making. The study addresses a complex replenishment problem involving joint pricing, environmental costs, order costs, preservation technology, and replenishment schedules for non-instantaneous deteriorating items, aiming to maximize retailer’s profit. Advanced optimization algorithms, including Ant Colony and Cuckoo Search, determine optimal pricing, investment costs, and replenishment schedules. Theoretical analysis and numerical experiments under a fuzzy learning environment provide a robust foundation for the model. Sensitivity analysis offers practical insights, guiding decision-makers in adapting strategies to real-world challenges. From a managerial perspec- tive, this study provides actionable solutions for balancing profitability with sustainability, ensuring efficient resource use during crises. It also highlights the importance of integrating environmental costs and preservation technology into inventory decisions, particularly in dynamic and uncertain environments like a pandemic. The study delivers comprehensive guidance for effective pandemic response planning, helping managers to make informed decisions that align with both economic and public health goals.