Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering (MSIE)

Degree Level

Graduate

Department

Industrial Engineering

Advisor/Mentor

Sullivan, Kelly M.

Committee Member

Liao, Haitao

Second Committee Member

Curry, Robert

Keywords

All-terminal Network; Monte Carlo Simulation; Multi-objective Bottleneck Spanning Tree Problem; Network Reliability; Survival Signature

Abstract

This research examines the problem of estimating the survival signature of all-terminal networks using Monte Carlo (MC) simulation. Following a recent similar result for twoterminal networks, we show that the work required within each MC replication corresponds to solving a multi-objective bottleneck spanning tree (MOBST) problem. We implement the resulting MC procedure using a “Blocks” algorithm from the literature to solve the MOBST in each replication by identifying its minimal set of non-dominated points. We compare this implementation against intuitive benchmark procedures for completing the work within an MC replication. We conduct numerical experiments to assess the efficacy of multi-objective optimization algorithms in expediting survival signature estimation for network structures. Our focus is to determine if these advanced computational strategies can significantly reduce the time complexity inherent in large-scale network analyses.

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