Enhancing pharmaceutical supply Chains in health crises: Integrating fuzzy logic and particle swarm optimization

During the pandemic, designing an efficient pharmaceutical supply chain network is essential to ensure timely and organized delivery of pharmaceutical inventory to patients at the minimum cost. Effective inventory control and management are crucial for reducing human casualties and improving patient health, particularly given the perishability of pharmaceutical products. This study addresses the uncertainties inherent in the pharmaceutical supply chain and focuses on optimizing these processes to reduce overall healthcare costs. Since pharmaceuticals represent a significant portion of healthcare expenditures worldwide, minimizing deterioration, and extending shelf life through advanced preservation techniques. Furthermore, the study explores how promotional efforts can increase awareness and demand for pharmaceutical products. Promotional activities can help ensure that patients and healthcare providers are informed about the availability and benefits of various medications, which is especially important during a pandemic. It also employs fuzzy logic to manage uncertainties in supply chain parameters such as holding costs, deterioration rates, and shortages. Our primary objective is to minimize the joint total cost for a pharmaceutical company and retailers in a fuzzy environment. We achieve this by determining deterioration rates, cycle time, production, pharmaceutical prices, preservation techniques, and promotional efforts in the pharmaceutical supply chain. To fulfill the objective of minimizing the cost of pharmaceutical inventory, we applied particle swarm optimization (PSO) algorithms using Python software. The study provides theoretical derivations and numerical examples, along with sensitivity analysis, to extract essential managerial insights into the impact of the pandemic.