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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

A Novel Approach To Orbital Debris Mitigation, Timothy S. Turk Dec 2022

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 …


A Study Of The Link Between Poor Quality And Poor Performance Of Various Health Care Indicators, Lamara L. Glass Dec 2022

A Study Of The Link Between Poor Quality And Poor Performance Of Various Health Care Indicators, Lamara L. Glass

Masters Theses

Abstract

Purpose of the Research

The purpose of present research investigation is to explore the correlation between poor performance indicators and poor quality in the Health care industry and to present an idea on improving the quality of the outcome in health care services by altering the KPIs.

The Nature of the Problem

The quality of healthcare may be evaluated using a variety of parameters, including safety, efficacy, and person-centeredness. For example, the former relates to the degree of precision with which procedures may produce desired results. External comparisons between different health care facilities, as well as KPIs specified for …


A Two-Phase Multicommodity Flow Approach For Classroom Assignment, Hannah Smith Dec 2022

A Two-Phase Multicommodity Flow Approach For Classroom Assignment, Hannah Smith

Masters Theses

A common problem faced by universities is the assignment of courses to campus-hosted spaces. An optimal solution is difficult to reach, given the number of constraints present. Increasing student enrollment across campus leads to a need for a model that optimally assigns courses to spaces that maximizes the total number of courses taught in person. This paper poses a solution to this problem by generating a two-phase model to allocate courses to campus-hosted spaces. The two-phase approach to classroom assignment consists of a minimum-weighted bipartite matching for priority assignment and a multicommodity flow model to assign remaining courses. The optimal …


Novel Mixed Integer Programming Approaches To Unit Commitment And Tool Switching Problems, Najmaddin Akhundov Dec 2022

Novel Mixed Integer Programming Approaches To Unit Commitment And Tool Switching Problems, Najmaddin Akhundov

Doctoral Dissertations

In the first two chapters, we discuss mixed integer programming formulations in Unit Commitment Problem. First, we present a new reformulation to capture the uncertainty associated with renewable energy. Then, the symmetrical property of UC is exploited to develop new methods to improve the computational time by reducing redundancy in the search space. In the third chapter, we focus on the Tool Switching and Sequencing Problem. Similar to UC, we analyze its symmetrical nature and present a new reformulation and symmetry-breaking cuts which lead to a significant improvement in the solution time. In chapter one, we use convex hull pricing …


Optimizing Strategic Planning With Long-Term Sequential Decision Making Under Uncertainty: A Decomposition Approach, Zeyu Liu Aug 2022

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 Aug 2022

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 May 2022

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 May 2022

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 …