Operations Research, Systems Engineering and Industrial Engineering Commons^™

A Novel Approach To Orbital Debris Mitigation, Timothy S. Turk

Doctoral Dissertations

Since mankind launched the first satellite into orbit in 1957, we have been inadvertently, yet deliberately, creating an environment in space that may ultimately lead to the end of our space exploration. Space debris, more specifically, orbital debris is a growing problem that must be dealt with sooner, rather than later. Several ideas have been developed to address the complex problem of orbital debris mitigation.

This research will investigate the possibility of removing orbital debris from the Low Earth Orbit (LEO) regime by using a metaheuristic algorithm to maximize collection of debris resulting from the February 2009 on-orbit collision of …

Optimizing Strategic Planning With Long-Term Sequential Decision Making Under Uncertainty: A Decomposition Approach, Zeyu Liu

Doctoral Dissertations

The operations research literature has seen decision-making methods at both strategic and operational levels, where high-level strategic plans are first devised, followed by long-term policies that guide future day-to-day operations under uncertainties. Current literature studies such problems on a case-by-case basis, without a unified approach. In this study, we investigate the joint optimization of strategic and operational decisions from a methodological perspective, by proposing a generic two-stage long-term strategic stochastic decision-making (LSSD) framework, in which the first stage models strategic decisions with linear programming (LP), and the second stage models operational decisions with Markov decision processes (MDP). The joint optimization …

Carbon Footprint And Cost Minimization For Grid Systems Through Day-Ahead Order And Battery Size Optimization, Omid Pourkhalili

Doctoral Dissertations

We modeled the problem of peak hours day-ahead order for smart grid companies integrating renewable energy and power storage systems. This results in optimizing day-ahead order, battery storage size, and consequently lowering the use of fossil fuels and emissions. The utility-scale power storage can balance the difference between the day-ahead forecasts and real-time consumer demand through energy arbitrage and transmission deferral for peaking capacity. We define system parameters and their associated costs and run a suggested algorithm to minimize the grid operating cost by optimizing day-ahead order amount and battery storage capacity. The model is designed to prioritize and take …

Optimization Methods For Day Ahead Unit Commitment, Jonathan David Schrock

Doctoral Dissertations

This work examines a variety of optimization techniques to better solve the day ahead unit commitment problem. The first method looks at the impact of almost identical generators on the problem and how to exploit that fact for computational gain. The second work seeks to improve the fidelity of the problem by better modeling the impact of pumped storage hydropower. Lastly, the relationship between the length of the planning horizon and the quality of the solutions is investigated.

Defect Detection For Additive Manufacturing With Machine Learning And Markov Decision Process, Rui Li

Doctoral Dissertations

Additive Manufacturing (AM) is a quickly evolving manufacturing technique in recent years. One of the most essential steps is the quality control of it. This involves the defect detection of the products, which is one of the bottlenecks that affects the high quality of AM products. One promising solution to this problem is to detect the defects in-situ and make decisions on the fly. We adopted Machine Learning (ML) algorithms for defect detection and develop a Markov Decision Process (MDP) model to make decisions for AM process. Our main purpose is to save costs and time through early termination or …