Sustainable systems engineering increasingly relies on quantitative models that connect methodological advances with real supply-chain decisions in energy and food systems. This dissertation integrates two pillars of systems engineering—optimization and supply-chain analysis—across three essays: (i) an AI/optimization framework that eliminates quantization error in fractal-dimension (FD) measurement from optical images, improving the fidelity of micro- to mesoscale structure quantification; (ii) a prospective life-cycle and systems analysis of reshoring crystalline-silicon photovoltaic (PV) manufacturing to the U.S., linking resilience with decarbonization; and (iii) a large-scale mixed-integer programming (MIP) model for locating and sizing fresh-produce hubs to support regional food security and cost-effective distribution. Together, these essays illustrate how reproducible analytics and optimization can inform resilient, lower-impact infrastructure across sectors.