This research investigates supply chain resilience through simulation-based optimization, focusing on the Capacitated Vehicle Routing Problem (CVRP) using Kuwait Danish Dairy Company (KDD) as a case study. It aims to enhance logistic robustness against disruptions such as demand spikes, traffic congestion, and regulatory restrictions. Using Google OR-Tools implemented in Python, scenarios representing baseline operations, urban congestion, demand surges, Ramadan operational constraints, and lockdown scenarios were simulated. The CVRP model minimizes total travel cost, considering vehicle capacity and route constraints to maintain cold chain integrity. Results highlighted significant impacts from compounded disruptions; traffic congestion increased total travel distance by 13%, while demand surges stretched operational limits. Ramadan scenarios required precise routing to meet delivery constraints within shorter timeframes. Lockdown scenarios showed reduced operational costs but potential revenue losses from inaccessible nodes. The comparative analysis demonstrated that optimized routing significantly mitigates disruption effects, improving efficiency by up to 20%. Recommendations include adaptive logistics strategies, real-time data integration, diversified supplier networks, and preemptive inventory management. The findings offer practical insights applicable broadly to food supply chains facing similar vulnerabilities in the Gulf region. This research advances the application of CVRP for enhancing supply chain resilience, offering a robust methodology and actionable solutions for managing logistic vulnerabilities. Future research directions include real-time route adjustments, multi-depot scenarios, and environmental sustainability considerations.