Abstract
The pharmaceutical sector is essential for ensuring universal access to medicines, demanding ef-ficient supply chains that deliver drugs at optimal prices with minimal delays and shortages. Pharmaceutical supply chains (PSCs) face significant challenges, including strict quality controls, government regulations, drug perishability, high R&D costs, and complex transportation require-ments. The sector is undergoing a shift, driven by the rise of pharmaceutical components in emerging markets, unpredictable demand, and reduced R&D investments by major companies, which struggle to compete with generic pharmaceutical brands. Post-pandemic challenges and geopolitical risks have further exposed vulnerabilities in PSCs, leading to frequent supply disrup-tions, product shortages, and unreliable transportation. The increasing focus on regionalization highlights the need for more resilient supply chains to manage disruptions effectively. PSCs must incorporate robustness to address uncertainties and disruptions, which can be internal, such as operational, financial, and quality risks, or external, including supply, demand, and environmental uncertainties. While recent research emphasizes the importance of integrating these uncertain-ties into decision-making tools, the field still lacks comprehensive solutions for PSC optimization. Previous studies have used heuristic and metaheuristic algorithms to address individual uncer-tainties and disruptions, providing feasible solutions with lower computational effort. However, a more holistic approach is required. This work introduces a new tool based on a Meta-heuristic al-gorithm, which evaluates the robustness of PSCs under multiple uncertainties and disruptions. The tool enables the development of what-if scenarios, helping decision-makers improve supply chain robustness and adaptability in the face of several risks