Graduation Semester and Year

2020

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Jay M Rosenberger

Abstract

Earthquakes are sudden and inevitable disasters that can cause enormous losses and suffering, and having accessible water is critically important for earthquake victims. To address this challenge, utility managers do preventive procedures on water pipes periodically to withstand future earthquake damage. The existing seismic vulnerability models usually consider simple methods to find the pipes to rehabilitate with highest priority. In this research, we develop an optimization approach to determine which water pipes to rehabilitate subject to a limited budget to achieve highest network serviceability after a disaster. We propose a two-stage stochastic mixed integer nonlinear program (MINLP). The MINLP model cannot be solved by commercial optimization software, like BARON even for problems with a very small number of scenarios. Consequently, we propose piecewise linear functions (PLF) to approximate the nonlinearity in the MINLP. Therefore, we formulate a mixed integer linear program (MILP) to approximate the MINLP. The optimization of the MILP is still challenging to solve, so we introduce a sequential heuristic algorithm to mitigate this computational issue and find bounds for the approximated optimal solution. Consequently, the solution we find using the sequential algorithm is within 2% of optimality.

Keywords

Network optimization, Two-stage stochastic programming, Linear approximation, Mixed integer non linear programming (MINLP), Decision analysis.

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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