Date of Graduation

12-2022

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering (MSIE)

Degree Level

Graduate

Department

Industrial Engineering

Advisor

Edward A. Pohl

Committee Member

Haitao Liao

Second Committee Member

Kelly Sullivan

Keywords

Machine Learning, Network Reliability, Reinforcement Learning, Resource Allocation

Abstract

Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal Policy Optimization (PPO) was used to identify the edge to be improved or a new edge to be added based on the current state of the network and the available budget. To calculate the network’s all-terminal reliability, a reliability polynomial was employed. To understand how the model behaves under a variety of conditions, we explored numerous network configurations with different initial link reliability, added link reliability, number of nodes, and budget structures. We conclude with a discussion of insights gained from our set of designed experiments.

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