A hybrid simulation-optimization decision (HySOD) framework for resilient and sustainable supply chain management

Global supply chains face increasing exposure to demand volatility, capacity constraints, and transportation disruptions, yet existing frameworks struggle to ensure both mathematical optimality and operational feasibility under real-world conditions. Optimization models generate prescriptive solutions without guaranteeing executability, while simulation models capture operational realism but lack prescriptive power. This study addresses this gap by developing and validating HySOD (Hybrid Simulation-Optimization Decision). This closed-loop simulation-based optimization system integrates Mixed-Integer Linear Programming with a multi-method simulation platform combining system dynamics, agent-based modelling, and discrete-event simulation. The framework embeds optimizer-generated shipment allocations as runtime parameters within the simulation environment, applies an evolutionary multi-objective algorithm for Pareto-optimal resilience strategy identification, and employs Monte Carlo testing for stochastic robustness validation, all executed against 14 weeks of real factory-warehouse-customer operational data. Statistical validation across 30 replications confirmed significant improvements over the baseline: service level rose from 85.1% to 90.0%, stockout rate fell by 46%, the resilience index improved by seven percentage points, and total cost decreased modestly. Three primary contributions are made: a closed-loop simulation-optimization architecture; a tri-layer linear programming model producing auditable, directly executable shipment plans; and a systematic Pareto-based evaluation: buffer stock, just-in-time scheduling, flexible manufacturing, redundant routing, and dynamic pricing under stochastic supply chain disruptions. The findings demonstrate that bridging mathematical optimization with operational simulation produces supply chain decisions that are simultaneously cost-efficient, executable, and resilient.