2026-09-18T21:06:31Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1210872026-05-15T17:53:31Zcom_1813_35col_1813_47
Liang, Haoyue
Gomez, Miguel
Li, Shanjun
Gao, Huaizhu
2026-04-03T18:53:17Z
2026-04-03T18:53:17Z
2025-12
ProQuest Submission ID: 15379
ProQuest Publication ID: 32395327
https://hdl.handle.net/1813/121087
https://doi.org/10.7298/yr2k-4566
17423289
122 pages
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.
en
AI Framework
Energy
Optimization
Supply Chain
Sustainability
ESSAYS ON OPTIMIZATION AND SUPPLY CHAIN STRATEGIES FOR SUSTAINABLE SYSTEMS: INSIGHTS FROM FRACTAL DIMENSION IMAGE ANALYSIS, PHOTOVOLTAIC MANUFACTURING RESILIENCE, AND FRESH PRODUCE INFRASTRUCTURE
dissertation or thesis
application/pdf
Systems Engineering
Cornell University
Doctor of Philosophy
Ph. D., Systems Engineering
https://hdl.handle.net/1813/59810.2
Publication
Cornell Theses and Dissertations
Liang, Haoyue
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
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<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>
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