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Optimizing Wedding Venue Selection Process Using Integer Programming, Luis Rodriguez 2023 University of Nebraska at Omaha

Optimizing Wedding Venue Selection Process Using Integer Programming, Luis Rodriguez

Theses/Capstones/Creative Projects

Choosing the right wedding venue can be extremely difficult for the unsuspecting engaged couple. There is a myriad of variables that must be taken into account prior to the illustrious wedding date; these variables include the option for a reception, the location, and food requirements, to name a few. Consequently, the typical couple seems to spend multiple months researching and visiting many wedding spaces. However, even though months go into planning, it still is not a guarantee that all variables are accounted for. Furthermore, without a wedding planner, these couples may second-guess their chosen site due to seemingly arduous issues …


Factors Influencing Users’ Attitudes Towards Using Brain Computer Interface (Bci) For Non Medical Uses: An Application Of The Technology Acceptance Model (Tam), Yichin Wu, Leila Halawi 2023 Embry-Riddle Aeronautical University

Factors Influencing Users’ Attitudes Towards Using Brain Computer Interface (Bci) For Non Medical Uses: An Application Of The Technology Acceptance Model (Tam), Yichin Wu, Leila Halawi

National Training Aircraft Symposium (NTAS)

While brain-computer interfaces (BCI) are gaining popularity in assisting people with illnesses, there is also increased technical research on incorporating BCI into healthy people’s lives. So far, not much research has focused on user attitudes, although some research has pointed out privacy and trust issues. Understanding potential users’ attitudes, expectations, and concerns early in the technology development stage is crucial for the novelty's success. For this reason, this study aims to understand the general publics’ attitude towards BCI for nonmedical uses using the technology acceptance model (TAM). The study will offer insights into how external factors including technology optimism, familiarity, …


Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz 2022 New Jersey Institute of Technology

Integrated Machine Learning And Optimization Approaches, Dogacan Yilmaz

Dissertations

This dissertation focuses on the integration of machine learning and optimization. Specifically, novel machine learning-based frameworks are proposed to help solve a broad range of well-known operations research problems to reduce the solution times. The first study presents a bidirectional Long Short-Term Memory framework to learn optimal solutions to sequential decision-making problems. Computational results show that the framework significantly reduces the solution time of benchmark capacitated lot-sizing problems without much loss in feasibility and optimality. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, …


Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli 2022 Army Cyber Institute, U.S. Military Academy

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

ACI Journal Articles

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …


Novel Mixed Integer Programming Approaches To Unit Commitment And Tool Switching Problems, Najmaddin Akhundov 2022 University of Tennessee, Knoxville

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 …


Essays On Perioperative Services Problems In Healthcare, Amogh S. Bhosekar 2022 Clemson University

Essays On Perioperative Services Problems In Healthcare, Amogh S. Bhosekar

All Dissertations

One of the critical challenges in healthcare operations management is to efficiently utilize the expensive resources needed while maintaining the quality of care provided. Simulation and optimization methods can be effectively used to provide better healthcare services. This can be achieved by developing models to minimize patient waiting times, minimize healthcare supply chain and logistics costs, and maximize access. In this proposal, we study some of the important problems in healthcare operations management. More specifically, we focus on perioperative services and study scheduling of operating rooms (ORs) and management of necessary resources such as staff, equipment, and surgical instruments. We …


On Variants Of Sliding And Frank-Wolfe Type Methods And Their Applications In Video Co-Localization, Seyed Hamid Nazari 2022 Clemson University

On Variants Of Sliding And Frank-Wolfe Type Methods And Their Applications In Video Co-Localization, Seyed Hamid Nazari

All Dissertations

In this dissertation, our main focus is to design and analyze first-order methods for computing approximate solutions to convex, smooth optimization problems over certain feasible sets. Specifically, our goal in this dissertation is to explore some variants of sliding and Frank-Wolfe (FW) type algorithms, analyze their convergence complexity, and examine their performance in numerical experiments. We achieve three accomplishments in our research results throughout this dissertation. First, we incorporate a linesearch technique to a well-known projection-free sliding algorithm, namely the conditional gradient sliding (CGS) method. Our proposed algorithm, called the conditional gradient sliding with linesearch (CGSls), does not require the …


Off-Policy Evaluation For Action-Dependent Non-Stationary Environments, Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip Thomas 2022 Army Cyber Institute, U.S. Military Academy

Off-Policy Evaluation For Action-Dependent Non-Stationary Environments, Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro Da Silva, Emma Brunskill, Philip Thomas

ACI Journal Articles

Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such methods because real-world problems are often subject to changes due to external factors (passive non-stationarity), changes induced by interactions with the system itself (active non-stationarity), or both (hybrid non-stationarity). In this work, we take the first steps towards the fundamental challenge of on-policy and off-policy evaluation amidst structured changes due to active, passive, or hybrid non-stationarity. Towards this goal, we make a higher-order stationarity assumption such that non-stationarity results in changes over time, but the way …


Using Strategic Options Development And Analysis (Soda) To Understand The Simulation Accessibility Problem, Andrew J. Collins, Ying Thaviphoke, Antuela A. Tako 2022 Old Dominion University

Using Strategic Options Development And Analysis (Soda) To Understand The Simulation Accessibility Problem, Andrew J. Collins, Ying Thaviphoke, Antuela A. Tako

Engineering Management & Systems Engineering Faculty Publications

Simulation modelling is applied to a wide range of problems, including defense and healthcare. However, there is a concern within the simulation community that there is a limited use and implementation of simulation studies in practice. This suggests that despite its benefits, simulation may not be reaching its potential in making a real-world impact. The main reason for this could be that simulation tools are not widely accessible in industry. In this paper, we investigate the issues that affect simulation modelling accessibility through a workshop with simulation practitioners. We use Strategic Options Development and Analysis (SODA), a problem-structuring approach that …


Retention Prediction And Policy Optimization For United States Air Force Personnel Management, Joseph C. Hoecherl 2022 Air Force Institute of Technology

Retention Prediction And Policy Optimization For United States Air Force Personnel Management, Joseph C. Hoecherl

Theses and Dissertations

Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets over time as they depart. To improve retention predictions, we develop and test traditional random forest models and feedforward neural networks as well as partially autoregressive forms of both, outperforming the benchmark on a test dataset by 62.8% and 34.8% for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted …


Optimizing Incentives For Systems With Heterogeneous Agents, Chen Chen 2022 New Jersey Institute of Technology

Optimizing Incentives For Systems With Heterogeneous Agents, Chen Chen

Dissertations

This dissertation explores new models and applications based on the game theory of incentives. This exploration starts with controlling an invasive insect problem to address one of the most significant challenges facing our forests, the invasion of the Emerald ash borer (EAB), a non-native, wood-boring insect that threatens to kill most ash trees in North America, through designing two new cost-sharing programs between the landowners and local governments. Ash trees are one of North America’s most widely distributed tree genera and a vital part of the green infrastructure of cities, where they provide residents with numerous social, economic, and ecological …


Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari 2022 Mississippi State University

Developing Novel Optimization And Machine Learning Frameworks To Improve And Assess The Safety Of Workplaces, Amin Aghalari

Theses and Dissertations

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model's robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to …


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

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 …


Dynamic Dilemma Zone Protection System: A Smart Machine Learning Based Approach To Countermeasure Drivers's Yellow Light Dilemma, Md Maynur Rahman 2022 University of South Alabama

Dynamic Dilemma Zone Protection System: A Smart Machine Learning Based Approach To Countermeasure Drivers's Yellow Light Dilemma, Md Maynur Rahman

Theses and Dissertations

Drivers’ indecisions within the dilemma zone (DZ) during the yellow interval is a major safety concern of a roadway network. The present study develops a systematic framework of a machine learning (ML) based dynamic dilemma zone protection (DZP) system to protect drivers from potential intersection crashes due to such indecisions. For this, the present study first develops effective methods of quantifying DZ using important site-specific characteristics of signalized intersections. By this method, high-risk intersections in terms of DZ crashes could be identified using readily available intersection site-specific characteristics. Afterward, the present study develops an innovative framework for predicting driver behavior …


Park Equity Modeling: A Case Study Of Asheville, North Carolina, Anisa Young 2022 Clemson University

Park Equity Modeling: A Case Study Of Asheville, North Carolina, Anisa Young

All Theses

Parks and greenspaces are publicly available entities that serve the vital purpose of promoting multiple aspects of human welfare. Unfortunately, the existence of park disparities is commonplace within the park setting. Specifically, marginalized individuals encounter limited park access, insufficient amenity provision, and poor maintenance. To remedy these disparities, we propose a process in which we select candidate park facilities and utilize facility location models to determine the optimal primary parks from both existing and candidate sites.

We note that platforms currently exist to identify the geographical areas where residents lack sufficient access to parks. However, these platforms do not yet …


Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde 2022 Clemson University

Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde

All Dissertations

A manufacturing company's inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation.

The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer's geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw …


Adaptive Design And Flexible Approval Of Clinical Trials, Saeid Delshad Sisi 2022 Clemson University

Adaptive Design And Flexible Approval Of Clinical Trials, Saeid Delshad Sisi

All Dissertations

Dose-finding clinical trials are among the most critical cornerstones of the healthcare system. In this broad research area, there are many decision making problems that are extremely challenging to address. However, a small improvement may result in significant benefits to the society. Dose-finding clinical trials are extremely expensive and require multiple time-consuming and complicated R&D phases. Despite all the costs and the long time these trials need to conclude (on average over ten years for each new drug/technology), only less than 15\% of these trials successfully end up in a new approved drug entering the market. This problem is even …


Implementation Of Lean And Six Sigma Methodologies To Improve The Operations And Efficiencies Of An Inpatient Pharmacy, Brian Ricks 2022 Western Michigan University Homer Stryker M.D. School of Medicine

Implementation Of Lean And Six Sigma Methodologies To Improve The Operations And Efficiencies Of An Inpatient Pharmacy, Brian Ricks

Medical Engineering Theses

Reducing overhead costs and eliminating process waste are important aspects of any successful organization. Hospital processes are frequently interconnected, with many exchanges of both materials and information between departments. The Inpatient Pharmacy, a key component of any hospital, has hundreds of interactions between departments on any given day, and many processes see the pharmacy acting as producers, consumers, and transporters of goods throughout the hospital. Such a varied role provides broad opportunities for process improvement. Within the broader manufacturing world, the philosophies of Lean and Six Sigma have helped many companies increase their process efficiencies, reduce costs, and increase quality. …


Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, shimaa mohamed ouf, Amira M. Idrees AMI 2022 BIS Helwan University

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …


Reconciliation, Restoration And Reconstruction Of A Conflict Ridden Country, Muhammad S. Riaz 2022 Air Force Institute of Technology

Reconciliation, Restoration And Reconstruction Of A Conflict Ridden Country, Muhammad S. Riaz

Theses and Dissertations

Conflict has sadly been a constant part of history. Winning a conflict and making a lasting peace are often not the same thing. While a peace treaty ends a conflict and often dictates terms from the winners’ perspective, it may not create a lasting peace. Short of unconditional surrender, modern conflict ends with a negotiated cessation of hostilities. Such accords may have some initial reconstruction agreements, but Reconciliation, Restoration and Reconstruction (RRR) is a long term process. This study maintains that to achieve a lasting peace: 1) The culture and beliefs of the conflict nation must be continuously considered and …


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