Operations Research, Systems Engineering and Industrial Engineering Commons^™

Dynamics Modeling Of Molten Salt Reactors, Visura Umesh Pathirana

Doctoral Dissertations

The abundance of energy is a necessity for the prosperity of humans. The rise in energy demand has created energy shortages and issues related to energy security. Nuclear energy can produce vast amounts of reliable energy without many of the negative externalities associated with other competing energy sources, such as coal and natural gas. As a result, public interest in nuclear power has increased in the past decade. Many new types of nuclear reactor are proposed. These nuclear reactor designs feature many passive technologies that can operate without external influence. Reactors that feature advanced passive safety features are catagorized as …

Utilization Of Integer Programming For Scheduling Maintenance At Nuclear Power Plants, Timothy Gallacher

Doctoral Dissertations

This thesis develops a thought that naturally explores three specific motifs for solving the complexities of scheduling maintenance at Nuclear Power Plants (NPP). The first chapter of this paper will develop the initial thought around creating a schedule for a given work week, including all the various constraints inherent to this problem. Such constraints include but are not limited to personnel availability, allowable component out-of-service time, and the Plant Risk Assessment. The objective function being to minimize the total cost of worker’s compensation for that given week.

The second chapter addresses the question of whether this simple schedule can be …

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …

Physics-Augmented Modeling And Optimization Of Complex Systems: Healthcare Applications, Jianxin Xie

Doctoral Dissertations

The rapid advances in sensing technology have created a data-rich environment that tremendously

benefits predictive modeling and decision-making for complex systems. Harnessing

the full potential of this complexly-structured sensing data requires the development of

novel and reliable analytical models and tools for system informatics. Such advancements in

sensing present unprecedented opportunities to investigate system dynamics and optimize

decision-making processes for smart health. Nevertheless, sensing data is typically

characterized by high dimensionality and intricate structures. To fully unlock the potential of

this data, we significantly rely on innovative analytical methods and tools that can effectively

process information.

The objective of this …

Scheduling Problem With Drying Requirements, Machine Eligibility Restrictions, Setup Times, And Assembly Requirements For An Injection Molding Facility, Ashley Owens

Doctoral Dissertations

Previous research only focused on an unrelated parallel machine scheduling problem with setup and processing resources. However, some manufacturing environments, such as plastic injection molding, need different sequential and parallel processes before the facility can process jobs in the machines. For example, some raw materials are hygroscopic, and a dryer must remove moisture before being processed in the injection molding machine. These dryers are portrayed as parallel machines. The job rather than the machine determines the drying time. Once the drying stage is complete and the raw materials are transferred to the actual machines to run jobs, the scheduling problem …

Improving Mobility And Safety In Traditional And Intelligent Transportation Systems Using Computational And Mathematical Modeling, Shahrbanoo Rezaei

Doctoral Dissertations

In traditional transportation systems, park-and-ride (P&R) facilities have been introduced to mitigate the congestion problems and improve mobility. This study in the second chapter, develops a framework that integrates a demand model and an optimization model to study the optimal placement of P&R facilities. The results suggest that the optimal placement of P&R facilities has the potential to improve network performance, and reduce emission and vehicle kilometer traveled. In intelligent transportation systems, autonomous vehicles are expected to bring smart mobility to transportation systems, reduce traffic congestion, and improve safety of drivers and passengers by eliminating human errors. The safe operation …

A Study Of The Effect Of Machine Parameters On Defects Produced In Eos Additive Manufacturing Builds, Tina White Malone

Doctoral Dissertations

⁵Additive Manufacturing (AM) is defined in the American Society for Testing and Materials (ASTM) standard F2792 as “a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies. It provides an advanced method for building complex geometries and parts for high performance with a significant cost savings. ⁵⁵It’s advantages include the reduced need for tools and molds commonly used in manufacturing, a large reduction in wasted material, much shorter manufacturing cycles for the building of hardware, and its uniquely inherent ability to produce much more complex shapes. …

Monitoring Additive Manufacturing Machine Health, Jeremy Hale

Doctoral Dissertations

Additive manufacturing (AM) allows the production of parts and goods with many benefits over more conventional manufacturing methods. AM permits more geometrically complex designs, custom and low-volume production runs, and the flexibility to produce a wide variety of parts on a single machine with reduced pre-production cost and time requirements. However, it can be difficult to determine the condition, or health, of an AM machine since complex designs can increase the variability of part quality. With fewer parts produced, destructive testing is less desirable and statistical methods of tracking part quality may be less informative. Combined with the relatively more …

Smart Warehousing Implementaion And Education Of Supply Chain Management Leaders, David Quintanilla

Doctoral Dissertations

Paper #1 Overview

Businesses have to adapt to new challenges and technologies in the marketplace which influence warehousing. In order to support this growth, Industry 4.0 technologies have been implemented along the value chain to optimize their organizations and production processes; however, there are still gaps for warehousing research for Industry 4.0. We present four pillars¾location strategy, infrastructure/design, data management, and advanced planning and control¾ as a framework for businesses to use for their adaptation into smart warehousing. In particular, this framework will guide companies in their logistics journey into Industry 4.0. Industry experts and senior logistics professionals were interviewed …

Exploiting Symmetry In Linear And Integer Linear Programming, Ethan Jedidiah Deakins

Doctoral Dissertations

This thesis explores two algorithmic approaches for exploiting symmetries in linear and integer linear programs. The first is orbital crossover, a novel method of crossover designed to exploit symmetry in linear programs. Symmetry has long been considered a curse in combinatorial optimization problems, but significant progress has been made. Up until recently, symmetry exploitation in linear programs was not worth the upfront cost of symmetry detection. However, recent results involving a generalization of symmetries, equitable partitions, has made the upfront cost much more manageable.

The motivation for orbital crossover is that many highly symmetric integer linear programs exist, and …

Mitigating Space Industry Supply Chain Risk Thru Risk-Based Analysis, Jennifer S. Cooper

Doctoral Dissertations

Using risk-based analysis to consider supply chain disruptions and uncertainty along with potential mitigation strategies in the early stages of space industry projects can be used avoid schedule delays, cost overruns, and lead to successful project outcomes.

Space industry projects, especially launch vehicles, are complicated assemblies of high-technology and specialized components. Components are engineered, procured, manufactured, and assembled for specific missions or projects, unlike make-to-stock manufacturing where assemblies are produced at a mass production rate for customers to choose off the shelf or lot, like automobiles.

The supply chain for a space industry project is a large, complicated web where …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang

Doctoral Dissertations

This study aims to investigate the spatiotemporal dynamic of global wildfires, their underlying climate-driving mechanisms, and their predictability by utilizing multiple data sources (both process-based model simulations and satellite-based observations) and multiple analytical methods including machine learning techniques (MLTs).

We first explored the global wildfire interannual variability (IAV) and its climate sensitivity across nine biomes from 1997 to 2018, leveraging the state-of-art U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) land component (ELM-v1) simulations with six sets of climate forcings. Results indicate that 1) ELM simulations could reproduce the IAV of wildfire in terms of magnitudes, distribution, bio-regional …

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

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

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

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

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 …

Identifying An Optimization Technique For Maker Usage To Address Covid-19 Supply Shortfalls, Michael J. Wilson

Doctoral Dissertations

Fused Deposition Modeling (FDM) can be purchased for under five hundred dollars. The availability of these inexpensive systems has created a large hobbyist (or maker) community. For makers, FDM printing is used numerous uses.

With the onset of the COVID-19 pandemic, the needs for Personal Protective Equipment (PPE) skyrocketed. COVID-19 mitigation strategies such as social distancing, businesses closures, and shipping delays created significant supply shortfalls. The maker community stepped in to fill gaps in PPE supplies.

In the case of 3DP, optimization remains the domain of commercial entities. Optimization is, at best, ad-hoc for makers. With the need to PPE …

Forecasting Nigeria's Electricity Demand And Energy Efficiency Potential Under Climate Uncertainty, Olawale Olabisi

Doctoral Dissertations

The increasing population and socio-economic growth of Nigeria, coupled with the current, unmet electricity demand, requires the need for power supply facilities expansion. Of all Nigeria’s electricity consumption by sector, the residential sector is the largest and growing at a very fast rate. To meet this growing demand, an accurate estimation of the demand into the future that will guide policy makers to adequately plan for the expansion of electricity supply and distribution, and energy efficiency standards and labeling must be made. To achieve this, a residential electricity demand forecast model that can correctly predict future demand and guide the …

Decomposition Approach To Parametric Nonconvex Regression; Nuclear Resonance Analysis, Jordan L. Armstrong

Masters Theses

Parameterized nonconvex regression is a difficult problem for any optimization solver packages, often resulting in approximations and linearizations of the problem in order to be able to arrive a solution, if the problem is even solvable at all. These changes to the initial problem are largely dependent upon having appropriate domain knowledge and still often times result in a sizable gap between the achieved solution and the best true solution. We propose a novel method of decomposing the global problem into small, overlapping windows. Thus, the independent windows are now solvable. Subsequently, we offer a novel, sequential method of parameter …

Improving Reinforcement Learning Techniques For Medical Decision Making, Matthew Baucum

Doctoral Dissertations

Reinforcement learning (RL) is a powerful tool for developing personalized treatment regimens from healthcare data. In RL, an agent samples experiences from an environment (such as a model of patient health) to learn a policy that maximizes long-term reward. This dissertation proposes methodological and practical developments in the application of RL to treatment planning problems.

First, we develop a novel time series model for simulating patient health states from observed clinical data. We use a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. We show that this model produces realistic …

Quality And Productivity Improvements In Additive Manufacturing, Huseyin Kose

Doctoral Dissertations

Additive manufacturing (AM) is a relatively new manufacturing technology compared to the traditional manufacturing methods. Even though AM processes have many advantages, they also have a series of challenges that need to be addressed to adapt this technology for a wide range of applications and mass production.

AM faces a number of challenges, including the absence of methods/models for determining whether AM is the best manufacturing process for a given part. The first study of this thesis proposes a framework for choosing specific AM processes by considering the complexity level of a part. It has been proven that the method …

Automated Warehouse Systems: A Guideline For Future Research, Wenquan Dong

Doctoral Dissertations

This study aims to provide a comprehensive tool for the selection, design, and operation of automated warehouse systems considering multiple automated storage and retrieval system (AS/RS) options as well as different constraints and requirements from various business scenarios.

We first model the retrieval task scheduling problem in crane-based 3D AS/RS with shuttle-based depth movement mechanisms. We prove the problem is NP-hard and find an optimality condition to facilitate the development of an efficient heuristic. The heuristic demonstrates an advantage in terms of solving time and solution quality over the genetic algorithms and the other two algorithms taken from literature. Numerical …

Optimization Of Islanded Utility-Microgrids After Natural Disasters, Rodney Kizito

Doctoral Dissertations

Natural disasters can cause widespread disturbances/power outages within distribution networks and hinder a utility’s ability to provide uninterrupted power supply to the critical public buildings (e.g., hospitals, grocery stores, fire, police and gas stations) within the utility’s serviced region. Backup generators, which are typically relied on during power interruptions, have limited capacities and have been reported to experience failures during usage. Microgrids, defined as localized power grids that incorporate distributed generators (DGs) and energy storage systems (ESSs) to allow them to operate independent of the main grid (i.e., island mode), can help utilities provide disaster relief power supply to critical …

Uht Milk: Supply Chain Based Shelf Life Assessment And Risk Mitigation, Sagar Rameshwar Padghan

Masters Theses

Transportation and storage conditions in the perishable food supply chain play a vital role in product shelf life. This study focuses on UHT milk, a variant of milk that has a shelf life of up to 12 months in ideal conditions. However, poor transportation and storage practices can diminish its shelf life and result in quality losses resulting from milk spoilage. UHT milk literature focuses on chemical and physical analysis of changes in milk. There have been limited number of studies that characterize supply chain effects on the shelf life of milk and other perishable products.

This study analyzes supply …

Periodic Replenish And Recount Policy To Address Record Inaccuracy From Stock Loss, Colton K. Ku

Masters Theses

Inventory record inaccuracy (IRI) often arises in retail environments due to unaccounted stock loss. Theft, misplacement, spoilage, and transaction errors will reduce the true inventory values without changing the inventory record. As previous inventory replenishment policies assume perfect record accuracy, increasing IRI can cause unexpected stockout events, mistimed reorders and replenishment freezes. Solutions to rectifying IRI vary from the use of improved tracking technologies to prevent it initially occurring at all to recounting programs which estimate true inventory value. Unfortunately, in retail environments, high‑tracking technology is unsuitable and continuous counting programs are too costly. To address the limitations of current …