Operations Research, Systems Engineering and Industrial Engineering Commons^™

Analyzing Responses From Likert Surveys And Risk-Adjusted Ranking: A Data Analytics Perspective, Abhijit Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

We broadly consider the topic of ranking entities from surveys/opinions. Often, numerous ranks from different respondents are available for the same entity, e.g., a candidate from a pool, and yet an averaging of those ranks may not serve the purpose of identifying a consensus candidate. We first consider a risk-adjusted paradigm for ranking, where the rank is defined as the average (mean) rank plus a scalar times the risk in the rank; we use standard deviation as a risk metric. In case of a candidate being ranked either on the basis of opinions of a selection committee's members or on …

Selecting Attributes, Rules, And Membership Functions For Fuzzy Sos Architecture Evaluation, Louis Pape, Siddhartha Agarwal, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The development of the FILA-SoS meta-architecture approach to acknowledged systems of systems (SoS) analysis allows a relatively unbiased method for exploring a potential SoS architecture space. This paper delves more deeply into the process of building the lists of desirable fuzzy attributes of a SoS, developing rules for combining attribute values to an overall assessment, and discovering membership function shapes that work well. A wide range of options exist for all the individual elements of SoS assessment. Some recommendations for finding an appropriate combination for the adjustable parameters of fuzzy assessment models through random architecture chromosome testing and iteration are …

Determination Of Rule Patterns In Complex Event Processing Using Machine Learning Techniques, Nijat Mehdiyev, Julian Krumeich, David Lee Enke, Dirk Werth, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Complex Event Processing (CEP) is a novel and promising methodology that enables the real-time analysis of stream event data. The main purpose of CEP is detection of the complex event patterns from the atomic and semantically low-level events such as sensor, log, or RFID data. Determination of the rule patterns for matching these simple events based on the temporal, semantic, or spatial correlations is the central task of CEP systems. In the current design of the CEP systems, experts provide event rule patterns. Having reached maturity, the Big Data Systems and Internet of Things (IoT) technology require the implementation of …

Empirical Study Utilizing Qfd To Develop An International Marketing Strategy, Cassandra C. Elrod, Sarah M. Stanley, Elizabeth A. Cudney, Caroline Fisher

Business and Information Technology Faculty Research & Creative Works

Customer expectations can be extremely nebulous. This research identifies the demographic segmentations and their respective expectations for the spa market. Interviews were conducted and a subsequent questionnaire was employed to gather the voice of the customer and assess the expectations of spa clientele. All of the data was collected from luxury spas in Hawaii, and consisted of respondents from all over the world. Results of the survey suggest that while men want more tangible benefits of a spa, women prefer the experiential qualities of peace and rejuvenation. Additionally, those of lower to middle income focus on the atmosphere more than …

Evaluating Generated Risk Event Effect Neutralization As A New Mitigation Strategy Tool In The Upstream Industry, Mohammad A. Alkazimi, Hanan Altabbakh, Susan L. Murray, Katie Grantham

Engineering Management and Systems Engineering Faculty Research & Creative Works

The upstream industry uses diverse risk mitigation approaches to mitigate eventual failures within its facilities. Yet, these approaches could not avert major accidents, on different scales, from happening as they negatively affect the industry. The purpose of this paper is to assess Generated Risk Event Effect Neutralization (GREEN) as a new tool to select suitable risk mitigation approach to prevent prospective failures in upstream industry. More than 200 hundred major accidents in the industry underwent GREEN evaluation and compared with existing risk mitigation approaches used in to mitigate eventual failures. Kuwait's’ Mina Al-Ahmadi explosion was chosen as a case study …

Engaging Healthcare Users Through Gamification In Knowledge Sharing Of Continuous Improvement In Healthcare, Elizabeth A. Cudney, Susan L. Murray, Connor M. Sprague, Lalaine M. Byrd, Frances M. Morris, Nathaniel Merwin, Debra L. Warner

Engineering Management and Systems Engineering Faculty Research & Creative Works

Knowledge management systems are key for capturing, retaining, and communicating results from projects and presenting information to staff. The purpose of a knowledge management system is to tap into the vast wisdom from projects and experts across an organization. This research focuses on the knowledge management system within the Veterans Health Administration that was developed as a repository of information on continuous improvement tools such as flowcharts, value stream mapping, 5S, and the application of these in healthcare projects. The use of social network analysis and gamification improves website organization, user participation, and dissemination of shared knowledge related to continuous …

“Rooming The Patient” Vs. “Moving The Patient", Susan L. Murray, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

Healthcare is coming under ever increasing scrutiny for cost, quality, safety, and patient satisfaction. This paper compares two operational models (“rooming the patience” vs. “moving the patient”) against productivity, privacy, user satisfaction, and other performance measurements. Varying risk factors for patient populations ranging from infants to geriatrics and medical specialties from mental health to orthopedics are addressed for both models. In the first operational model after checking-in the patient is escorted to an examination room and waits as various caregivers (nurses, doctors, clerks, etc.) come and go from the exam room. In the second model the caregivers work from a …

Blended Classes: Expectations Vs. Reality, Susan L. Murray, Julie Phelps, Kelly L. Jones

Engineering Management and Systems Engineering Faculty Research & Creative Works

Blended courses, also called hybrid, have a portion of the course taught face-to-face in a classroom, and at least one-third of the course work is online. Some instructors consider this format to be "the best of both worlds." Students receive the personal contact and interaction with the instructor during the classroom portion. They also have flexibility in the pace, access, and repetition of the online content. In this paper, we explore 49 graduate students' expectations for a required operations management course that was delivered in a blended format. The same students were also surveyed at the completion of the course …

A Systematic Review Of Technological Advancements To Enhance Learning, Elizabeth A. Cudney, Julie Ezzell

Engineering Management and Systems Engineering Faculty Research & Creative Works

Assessing student learning styles and incorporating thought-provoking activities has been a focus of research for years. Virtual technology and social media are transforming traditional classrooms into training spaces that can be tailored for individual learning patterns and personalized for different skill levels. These technological tools are not only revolutionizing the conventional lecture-based classroom but also beginning to incorporate options such as flipped and blended classrooms. Students in these nontraditional settings are given additional hands-on experience that allows them to become immersed in a variety of subjects. Flipped classrooms in particular use class time effectively by challenging students to prepare prior …

Arbitrary Category Classification Of Websites Based On Image Content, Anton Akusok, Yoan Miche, Juha Karhunen, Kaj Mikael Björk, Rui Nian, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Comprehensive Methodology for General Large-Scale Image-Based Classification Tasks. It Addresses the Big Data Challenge in Arbitrary Image Classification and More Specifically, Filtering of Millions of Websites with Abstract Target Classes and High Levels of Label Noise. Our Approach Uses Local Image Features and their Color Descriptors to Build Image Representations with the Help of a Modified K-Nn Algorithm. Image Representations Are Refined into Image and Website Class Predictions by a Two-Stage Classifier Method Suitable for a Very Large-Scale Real Dataset. a Modification of an Extreme Learning Machine is Found to Be a Suitable Classifier Technique. the …

Incentive-Based Negotiation Model For System Of Systems Acquisition, Nil Kilicay-Ergin, Cihan Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Lack of collaboration between individual systems and systems of systems (SoS) program management is identified as one of the leading problems in SoS acquisition. This is especially a major concern in acknowledged SoS where a designated SoS program management has no authority over the constituent systems. Therefore, it is important to consider mechanisms to persuade individual systems to participate in the SoS development. In SoS where individual systems have their own self-interests, negotiation becomes an important mechanism to increase participation in SoS development. Another mechanism, incentives, is used in a wide range of applications to improve performance and collaboration. In …

A Computational Intelligence Approach To System-Of-Systems Architecting Incorporating Multi-Objective Optimization, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions …

Meme Representations For Game Agents, Yoan Miche, Meng Hiot Lim, Amaury Lendasse, Yew Soon Ong

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Advancement in Game Technology Has Served to Enrich Player's Gaming Experience in a Substantial Way. Nowadays, it is Common to Have Blockbuster Quality Games, with Realistic Graphics and Engaging Stories. Despite This, the Progress Made in Incorporating Artificial Intelligence Has Been Slow, and Realistic Human-Like Intelligence in Games is Hardly to Be Found. There Have Been Some Attempts to Use Machine Learning in Games, But Such Attempts Often Ended Up Impractical or Affecting the Players Enjoyment Due to Several Constraining Factors. in This Paper, We Describe Meme War as a Proof-Of-Concept for Practical Usage of Machine Learning in Games. …

Flexible And Intelligent Learning Architectures For Sos (Fila-Sos), Cihan H. Dagli, David Lee Enke, Nil Ergin, Dincer Konur, Ruwen Qin, Abhijit Gosavi, Renzhong Wang, Louis Pape Ii, Siddhartha Agarwal, Ram Deepak Gottapu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) …

Defensive Routines In Engineering Managers And Non-Engineering Managers -- A Case Analysis, Tyria Riley, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

There is a perception that engineers experience challenges in areas such as communication, conflict resolution, and leadership. Defensive routines are actions implemented as a result of being in an embarrassing or threatening situation. This research uses a case study approach to measure whether defensive routines are more common in engineering managers or non-engineering managers. Twenty-seven managers created case studies based on their unique experiences as managers. These case studies were scored, and the results of this research indicate that engineering managers employ defensive routines more commonly than non-engineering managers.

Extreme Learning Machine On High Dimensional And Large Data Applications, Zhiping Lin, Jiuwen Cao, Tao Chen, Yi Jin, Zhan Li Sun, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

Particle Swarm Optimization Based Selective Ensemble Of Online Sequential Extreme Learning Machine, Yang Liu, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

A Novel Particle Swarm Optimization based Selective Ensemble (PSOSEN) of Online Sequential Extreme Learning Machine (OS-ELM) is Proposed. It is based on the Original OS-ELM with an Adaptive Selective Ensemble Framework. Two Novel Insights Are Proposed in This Paper. First, a Novel Selective Ensemble Algorithm Referred to as Particle Swarm Optimization Selective Ensemble is Proposed, noting that PSOSEN is a General Selective Ensemble Method Which is Applicable to Any Learning Algorithms, Including Batch Learning and Online Learning. Second, an Adaptive Selective Ensemble Framework for Online Learning is Designed to Balance the Accuracy and Speed of the Algorithm. Experiments for Both …

High-Performance Extreme Learning Machines: A Complete Toolbox For Big Data Applications, Anton Akusok, Kaj Mikael Bjork, Yoan Miche, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Complete Approach to a Successful Utilization of a High-Performance Extreme Learning Machines (Elms) Toolbox for Big Data. It Summarizes Recent Advantages in Algorithmic Performance; Gives a Fresh View on the Elm Solution in Relation to the Traditional Linear Algebraic Performance; and Reaps the Latest Software and Hardware Performance Achievements. the Results Are Applicable to a Wide Range of Machine Learning Problems and Thus Provide a Solid Ground for Tackling Numerous Big Data Challenges. the Included Toolbox is Targeted at Enabling the Full Potential of Elms to the Widest Range of Users.

Efficient Skin Segmentation Via Neural Networks: Hp-Elm And Bd-Som, C. Swaney, A. Akusok, K. M. Björk, Y. Miche, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents Two Novel Methods for Skin Detection: Hp-Elm and Bd-Som. Both Som and Elm Are Fast for Large Data Sets, But Not Yet Suitable for Big Data. We Show How They Can Be Improved in Order to Fulfill the Strict Requirements for Big Data. Both New Methods Are Described and their Implementations Are Explained. a Comparison on a Large Example is Presented in the Experiment Section. We Find that Bd-Som is More Accurate but Not as Computationally Efficient as Hp-Elm. as a Result, We Show that Both Methods Work Well on a Big Data Task. the Given Task …

Rote-Lcs Learning Classifier System For Classification And Prediction, Benjamin Daniels

Masters Theses

"Machine Learning (ML) involves the use of computer algorithms to solve for approximate solutions to problems with large, complex search spaces. Such problems have no known solution method, and search spaces too large to allow brute force search to be feasible. Evolutionary algorithms (EA) are a subset of machine learning algorithms which simulate fundamental concepts of evolution. EAs do not guarantee a perfect solution, but rather facilitate convergence to a solution of which the accuracy depends on a given EA's learning architecture and the dynamics of the problem.

Learning classifier systems (LCS) are algorithms comprising a subset of EAs. The …

A Valuation Model For Two-Stage Contract Negotiations Over Multiple Interdependent Issues, Nnaemeka U. Amaeshi

Masters Theses

"Most real-world negotiation scenarios involve multiple interdependent issues over which the negotiating parties will seek an agreement. Simultaneous negotiation over multiple interdependent issues is an especially challenging problem because utility functions of negotiating agents are typically nonlinear and difficult to analyze. Also, negotiations often happen under circumstances of incomplete information, where either party has either little or inaccurate information about the preferences or utilities of the other party. Consequently, negotiations may go over multiple rounds until a mutually acceptable agreement is reached. The objective of this research is to create a quantitative model of decision-making that helps one negotiating party …

A Decision Support Simulation Model For Bed Management In Healthcare, Raja A. Baru

Masters Theses

"In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources such as beds. Bed management is key to the effective delivery of high-quality and low-cost healthcare. An efficient utilization of beds requires a detailed understanding of the hospital's operational behavior. It is necessary to understand the behavior of a hospital in order to make necessary adjustments to its resources, and policies, which can improve patient's access to care. The aim of this research was to develop a discrete event simulation to assist in planning and staff scheduling decisions. …

Utilizing Learning Style Preferences And Quality Function Deployment For Curriculum Development, Julie M. Ezzell

Masters Theses

"Workplace requirements continually evolve to keep pace with the developing global market. To meet ever increasing standards, educational institutions have been investigating methods to prepare students for their future employment. Course modifications should be carefully considered to meet the requirements of all stakeholders, including those of the students. The objective of this research was to provide students with an overall better learning experience that tailors the teaching methods to his/her individual learning preferences. To meet this objective, a comprehensive survey was provided to an undergraduate course in quality. The survey documented the student's individuality when learning and made note of …

Detection And Recognition Of R/F Devices Based On Their Unintended Electromagnetic Emissions Using Stochastic And Computational Intelligence Methods, Shikhar Prasad Acharya

Doctoral Dissertations

"Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be thought of as a signature of the device. This property of uniqueness of UEEs can be used to detect and identify the device producing the emission. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band which makes them difficult to detect. There are two types of UEE detection methods. The first one is called stimulated detection method where the UEEs of a device are enhanced using …

Modeling Supply Chain Interdependent Critical Infrastructure Systems, Varun Ramachandran

Doctoral Dissertations

"While strategies for emergency response to large-scale disasters have been extensively studied, little has been done to map medium- to long-term strategies capable of restoring supply chain infrastructure systems and reconnecting such systems from a local urban area to national supply chain systems. This is, in part, because no comprehensive, data-driven model of supply chain networks exists. Without such models communities cannot re-establish the level of connectivity required for timely restoration of goods and services. This dissertation builds a model of supply chain interdependent critical infrastructure (SCICI) as a complex adaptive systems problem. It defines model elements, data needs/element, the …

Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal

Doctoral Dissertations

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving …

Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal

Doctoral Dissertations

"Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.

A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover's iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.

The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon stochastic optimal …

The Engagement Of Expert Opinions In The Modeling Of Multi-Attribute Decision Making For The Selection Of Project Delivery Methods In Building Construction, Issam H. Algraiw

Doctoral Dissertations

"Choosing the most appropriate project delivery method (PDM) available is acknowledged as a crucial issue in the construction industry. Both the choice and application of an unsuitable PDM can result in project failure. Likewise, the selection of a suitable PDM can increase the chance for project success. A method of selecting from among the seven PDMs recognized by the Construction Specifications Institute (CSI) was created in an attempt to address this issue.

This research was comprised of three objectives. The first objective was to determine the influential factors needed to select the appropriate PDMs available to the US construction industry. …

Predicting Solar Irradiance Using Time Series Neural Networks, Ahmad Alzahrani, Jonathan W. Kimball, Cihan H. Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other …

Quantifying Economic Benefits For Rail Infrastructure Projects, Suzanna Long, Ruwen Qin, John Betak, Akhilesh Ojha, John J. Myers

Engineering Management and Systems Engineering Faculty Research & Creative Works

This project identifies metrics for measuring the benefit of rail infrastructure projects for key stakeholders. It is important that stakeholders with an interest in community economic development play an active role in the development of the rail network. Economic development activities in both rural and urban settings are essential if a nation is to realize growth and prosperity. Many communities have developed goals and visions to establish an economic development program, but they often fail to achieve their goals due to uncertainties during the project selection and planning process. Communities often select a project from a vast pool of ideas …