<p>Recent systemic disruptions, such as the COVID-19 pandemic, have highlighted the structural fragility of global supply chains in the face of risks such as supply chain disruptions or market access restrictions. Among the most significant risks are sudden surges in customer demand and the closure of regions where suppliers or customers are located. In this context, quantitative supply chain risk and performance management play a crucial role, aiming to anticipate and mitigate potential disruptions while promoting improved performance and sustainability. This study is aimed at developing effective quantitative management for a three-tiered supply chain, composed of suppliers, a distributor, and customers. The primary objective is to improve customer satisfaction by ensuring reliable supply, even under adverse conditions such as supplier closures or restricted access to customer areas due to risk events like a pandemic, while preserving the economic, environmental, and operational performance of the supply chain. To achieve this, we are developing a mixed-integer, multi-objective mathematical model that simultaneously minimizes total cost, reduces CO₂ emissions, and minimizes delivery times, based on three complementary criteria that collectively reflect the resilience and sustainability of the supply chain. The model includes a set of relevant constraints and is applied to two scenarios: an ideal, risk-free scenario, and a scenario with risk. Two solution approaches are used: the <i>ε</i>-constraint method and the Non-Dominated Genetic Sorting Algorithm II (NSGA-II).</p>