Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Industrial Engineering
Committee Chair/Advisor
Yongjia Song
Committee Member
Qi Luo
Committee Member
Weichiang Pang
Committee Member
Thomas Christos Sharkey
Committee Member
Emily Tucker
Abstract
This dissertation develops an integrated modeling and solution framework for direct temporary disaster housing logistics under demand uncertainty. The proposed framework addresses the problem from short-term, long-term, and computational perspectives. First, a two-stage chance-constrained stochastic programming (TSCC) model is developed for short-term housing logistic planning, with demand scenarios generated via a data-driven spatial regression model that captures the relationship between housing demand, hazard exposure, and socioeconomic factors. A case study based on Hurricane Ian demonstrates that the TSCC model outperforms deterministic and traditional scenario-based models in both solution quality and robustness. Second, to address long-term housing logistics planning, a multi-horizon stochastic programming (MHSP) model is proposed. This formulation captures short-term uncertainty through regression-based demand estimation and long-term uncertainty via time series analysis. Numerical results validate the MHSP model's ability to improve planning performance under uncertainty compared to conventional approaches. Finally, the framework is extended to a periodic multi-stage stochastic programming (PMSP) model that leverages the periodic nature of disaster occurrences to improve computational efficiency. Computational experiments using historical hurricane data demonstrate that the PMSP model achieves comparable solution quality to standard multi-stage stochastic programming while significantly reducing computational time.
Recommended Citation
Chen, Sheng-Yin, "Data-driven Stochastic Programming for Disaster Housing Logistics Planning" (2025). All Dissertations. 4029.
https://open.clemson.edu/all_dissertations/4029