Date of Award

Spring 1-1-2015

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Civil, Environmental & Architectural Engineering

First Advisor

Joseph R. Kasprzyk

Second Advisor

Balaji Rajagopalan

Third Advisor

Edith Zagona

Abstract

Deep uncertainty refers to situations in which decision makers do not know, or cannot agree upon, the full suite of risks within a planning problem. This thesis reviews frameworks proposed for planning under deep uncertainty. One framework, Many Objective Robust Decision Making (MORDM), combines two techniques: evolutionary algorithm search used to optimize planning alternatives and robust decision making used to sample performance over many plausible factors and choose a robust solution.

We present a methodology that modifies the uncertain input selected within the MOEA search process. Using a case study of water planning in the Lower Rio Grande Valley (LRGV) in Texas, this research uses visualization techniques to assess the performance of optimized alternatives across five scenarios to answer the following: (1) How do deep uncertainties impact the tradeoffs and decisions? and (2) What is the impact of experiencing futures unlike the optimized conditions?

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