{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/121087"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/121087","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"ESSAYS ON OPTIMIZATION AND SUPPLY CHAIN STRATEGIES FOR SUSTAINABLE SYSTEMS: INSIGHTS FROM FRACTAL DIMENSION IMAGE ANALYSIS, PHOTOVOLTAIC MANUFACTURING RESILIENCE, AND FRESH PRODUCE INFRASTRUCTURE","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Liang, Haoyue"],"institution":"Cornell University","degree_name":"Ph. 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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. 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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. 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