Sustainability and resilience are emerging as prime goals in contemporary supply chain management with rising environmental regulations, social pressures, and economic sustainability requirements. This paper introduces an advanced hybrid Mixed-Integer Linear Programming (MILP) model that combines circular economy concepts (closed-loop flows) and robust optimization methods to tackle uncertainty in demand, returns, and operational disruptions. The hybrid method uses a two-stage procedure: a metaheuristic pre-solver for large instances of the problem, followed by exact refinement with a commercial MILP solver. We perform extensive experiments under deterministic, stochastic, and disruption scenarios to compare the new model with traditional linear programming (LP), heuristic models, and genetic algorithms (GA). The results show that our approach achieves substantially higher cost savings, emissions reduction, and waste minimization with high service levels. A comparison chart and performance plots emphasize the benefits of operational efficiency, environmental footprint, and computational tractability. We conclude with a discussion on managerial implications, social sustainability integration, and future research directions, emphasizing the potential of hybrid and robust MILP for real-world sustainable supply chain design.