Mathematical Optimization for Climate Resilient Food Systems under Global Shocks

We examine whether mathematical optimization capability strengthens climate resilient food system performance under synchronized global shocks in India during 2020 to 2025. We construct a structured panel of 48 expert validated scenarios integrating robust optimization design, stochastic programming and scenario planning, and multi objective trade off optimization with global food inflation severity indicators. Using fixed effects estimation and moderated regression, we find that optimization capability significantly improves availability stability, access affordability, utilization nutrition quality, and supply chain adaptability. Multi objective optimization exhibits the strongest effect, followed by robust design, while stochastic programming provides adaptive buffering within modeled probability bounds. Global shock severity negatively affects resilience, yet interaction results show that optimization capability offsets a substantial share of this adverse impact. Our results demonstrate that optimization architecture functions as a structural resilience buffer rather than a technical efficiency tool. The contribution advances resilience theory by quantifying buffering elasticity under synchronized inflation and climate stress, offering policy guidance for institutionalizing integrated optimization systems in emerging economies.