Operations Research, Systems Engineering and Industrial Engineering Commons^™

Development Of Directed Randomization For Discussing A Minimal Security Architecture, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Akkarakaran Francis Leonard, Kip Nieman, Helen Durand, Katie Tyrrell, Katrina Hinzman, Michael Williamson

Chemical Engineering and Materials Science Faculty Research Publications

Strategies for mitigating the impacts of cyberattacks on control systems using a control-oriented perspective have become of greater interest in recent years. Our group has contributed to this trend by developing several methods for detecting cyberattacks on process sensors, actuators, or both sensors and actuators simultaneously using an advanced optimization-based control strategy known as Lyapunov-based economic model predictive control (LEMPC). However, each technique comes with benefits and limitations, both with respect to one another and with respect to traditional information technology and computer science-type approaches to cybersecurity. An important question to ask, therefore, is what the goal should be of …

Cybersecurity And Dynamic Operation In Practice: Equipment Impacts And Safety Guarantees, Kip Nieman, Dominic Messina, Matthew Wegener, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Though dynamic operation of chemical processes has been extensively explored theoretically in contexts such as economic model predictive control or even considering the potential for cyberattacks on control systems creating non-standard operating policies, important practical questions remain regarding dynamic operation. In this work, we look at two of these with particular relevance to process safety: (1) evaluating dynamic operating policies with respect to process equipment fidelity and (2) evaluating procedures for determining the parameters of an advanced control law that can promote both dynamic operation as well as safety if appropriately designed. Regarding the first topic, we utilize computational fluid …

On-Line Process Physics Tests Via Lyapunov-Based Economic Model Predictive Control And Simulation-Based Testing Of Image-Based Process Control, Henrique Oyama, Akkarakaran Francis Leonard, Minhazur Rahman, Govanni Gjonaj, Michael Williamson, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Next-generation manufacturing involves increas- ing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can …

An Elliptical Cover Problem In Drone Delivery Network Design And Its Solution Algorithms, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

Given n demand points in a geographic area, the elliptical cover problem is to determine the location of p depots (anywhere in the area) so as to minimize the maximum distance of an economical delivery trip in which a delivery vehicle starts from the nearest depot to a demand point, visits the demand point and then returns to the second nearest depot to that demand point. We show that this problem is NP-hard, and adapt Cooper’s alternating locate-allocate heuristic to find locally optimal solutions for both the point-coverage and area-coverage scenarios. Experiments show that most locally optimal solutions perform similarly …

Lyapunov-Based Economic Model Predictive Control For Online Model Discrimination, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Economic model predictive control (EMPC) is a flexible control design strategy that can be modified to achieve many operating goals while also ensuring safe operation (e.g., by adding Lyapunov-based stability constraints to form Lyapunov-based EMPC, or LEMPC). Prior works have investigated LEMPC capabilities for achieving goals online beyond optimizing process economics, including aiding in model structure selection to benefit model-based control system design since the accuracy and quality of the process model are important for achieving an expected performance from such systems. This work further probes the capabilities of LEMPC to accomplish multiple objectives during process operation, including aiding in …

Optimization-Based Uav Fleet Routing And Safety Assurance – Models, Algorithms, And Prototyping, Zhenyu Zhou

Wayne State University Dissertations

Unmanned aerial vehicles (UAVs), especially multi-rotor drones, have been increasingly used in various scenarios in the last decade. With the reduced hardware costs, improved battery life, and enhanced processor performance, we can eventually allow all kinds of drones to automatically travel through the low-altitude airspace. The large-scale application of drones will extend the basic transportation facilities from the ground to the air and form 3D transportation networks for the future. Compared to current ground-vehicle and aircraft traffic systems, multi-UAV systems are far from well-developed. Most current multi-UAV systems are human-operated or pre-programmed to perform specific tasks. The current application of …

Handling Of Stealthy Sensor And Actuator Cyberattacks On Evolving Nonlinear Process Systems, Henrique Oyama, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Cyberattacks on control systems in the chemical process industries cause concern regarding how they can impact finances, safety, and production levels of companies. A key practical challenge for cyberattack detection and handling using process information is that process behavior evolves over time. Conceivably, changes in process dynamics might cause some detection strategies to flag a change in the dynamics as an attack due to the new data appearing abnormal compared to data from before the dynamics changed. In this work, we utilize several case studies to probe the question of what might be the impacts, benefits, and limitations of cyberattack …

Integrated Cyberattack Detection And Handling For Nonlinear Systems With Evolving Process Dynamics Under Lyapunov-Based Economic Model Predictive Control, Keshav Kasturi Rangan, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Safety-critical processes are becoming increasingly automated and connected. While automation can increase effciency, it brings new challenges associated with guaranteeing safety in the presence of uncertainty especially in the presence of control system cyberattacks. One of the challenges for developing control strategies with guaranteed safety and cybersecurity properties under suffcient conditions is the development of appropriate detection strategies that work with control laws to prevent undetected attacks that have immediate closed-loop stability consequences. Achieving this, in the presence of uncertainty brought about by plant/model mismatch and process dynamics that can change with time, requires a fundamental understanding of the characteristics …

Medical Surge Capability: Performance Modeling Of Hospital Emergency Departments, Egbe-Etu Emmanuel Etu

Wayne State University Dissertations

Hospitals are faced with significant challenges during and after natural or human-caused disasters. Surge planning is a critical component of every healthcare facility’s emergency plan and response system. The process of managing and allocating scarce resources by tackling the vulnerability inherent to patients means that defining improvement priorities is one of the main challenges healthcare systems face when responding to a medical surge event (e.g., COVID-19). The consequences of these challenges include increased patient mortality, ambulance diversion, long wait times, and unavailability of beds. Previous efforts in hospital operations management have successfully applied operations research techniques in analyzing and optimizing …

Maximizing User Engagement In Short Marketing Campaigns Within An Online Living Lab: A Reinforcement Learning Perspective, Aniekan Michael Ini-Abasi

Wayne State University Dissertations

ABSTRACT

MAXIMIZING USER ENGAGEMENT IN SHORT MARKETING CAMPAIGNS WITHIN AN ONLINE LIVING LAB: A REINFORCEMENT LEARNING PERSPECTIVE

by

ANIEKAN MICHAEL INI-ABASI

August 2021

Advisor: Dr. Ratna Babu Chinnam Major: Industrial & Systems Engineering Degree: Doctor of Philosophy

User engagement has emerged as the engine driving online business growth. Many firms have pay incentives tied to engagement and growth metrics. These corporations are turning to recommender systems as the tool of choice in the business of maximizing engagement. LinkedIn reported a 40% higher email response with the introduction of a new recommender system. At Amazon 35% of sales originate from recommendations, …

Integrated Optimization And Learning Methods Of Predictive And Prescriptive Analytics, Mehmet Kolcu

Wayne State University Dissertations

A typical decision problem optimizes one or more objectives subject to a set of constraints on its decision variables. Most real-world decision problems contain uncertain parameters. The exponential growth of data availability, ease of accessibility in computational power, and more efficient optimization techniques have paved the way for machine learning tools to effectively predict these uncertain parameters. Traditional machine learning models measure the quality of predictions based on the closeness between true and predicted values and ignore decision problems involving uncertain parameters for which predicted values are treated as the true values.Standard approaches passing point estimates of machine learning models …

Framework For Effective Resilience Managmenet Of Complex Supply Networks, Elham Taghizadeh

Wayne State University Dissertations

In today's environment with high global and complex supply chains for engineered products, the ability to assess and manage the resilience of supply chains is not a luxury but a fundamental prerequisite for business continuity and success. This is particularly true for firms with deep-tier supply chains, such as the automotive original equipment manufacturers (OEMs) and their suppliers. Automotive supply networks are particularly facing growing challenges due to their complexity, globalization, economic volatility, rapidly changing technologies, regulations, and environmental/political shocks. These risks and challenges can disrupt and halt operations in any section of the supply network. Given that supply chains …

Intelligent Healthcare Process Discovery And Operational Coordination Using Discrete Event Simulation And Machine Learning, Suleyman Yildirim

Wayne State University Dissertations

The healthcare system in the US is rapidly changing and reshaping to adopt continuously evolving demand for improved operational efficiency and treatment effectiveness from patients and providers in critical health services. Healthcare service systems and clinical treatment operations need to be more predictable to increase operational efficiency through proactive operations management. This research contributes to the literature by discovering clinical processes and calibrating discrete-event simulation models in healthcare service systems using data-driven and process-driven predictive models. Unlike the data-driven predictive approaches such as machine learning and statistical methods, the proposed methodologies in this thesis leverages and focuses on process-based methods …

Customer Choice Modeling For Retail Category Assortment Planning And Product-Line Extension, Elham Nosratmirshekarlou

Wayne State University Dissertations

Growing competitiveness and increasing availability of data is generating great interest in data-driven analytics across industries. One of the areas that has gained a lot of attention is Customer choice modeling, which aims to explain the choices individual customers make in choosing from a set of products based on their preferences. While effective customer choice modeling is essential to a wide variety of application domains, including retail, it is challenging in practice due to limitations around the quality of the data available for modeling and potentially complex choice behaviors. This dissertation presents a hybrid modeling approach that relies on both …

Dynamic Resource Allocation For Coordination Of Inpatient Operations In Hospitals, Najibesadat Sadatijafarkalaei

Wayne State University Dissertations

Healthcare systems face difficult challenges such as increasing complexity of processes, inefficient utilization of resources, high pressure to enhance the quality of care and services, and the need to balance and coordinate the staff workload. Therefore, the need for effective and efficient processes of delivering healthcare services increases. Data-driven approaches, including operations research and predictive modeling, can help overcome these challenges and improve the performance of health systems in terms of quality, cost, patient health outcomes and satisfaction.

Hospitals are a key component of healthcare systems with many scarce resources such as caregivers (nurses, physicians) and expensive facilities/equipment. Most hospital …

Economic Model Predictive Control And Process Equipment: Control-Induced Thermal Stress In A Pipe, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy than under a steady-state operating policy. However, a concern for time-varying operation is how such a change in operating policy might impact the equipment within which the processes being controlled are carried out. While under steady-state operation, the operating conditions to which equipment would regularly be exposed can be estimated, this would be more difficult to assess thoroughly a priori under time-varying operation. It could be explored whether the EMPC could be made aware …

On Accounting For Equipment-Control Interactions In Economic Model Predictive Control Via Process State Constraints, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Traditionally, chemical processes have been operated at steady-state; however, recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy. It is unclear how time-varying operating policies may impact process equipment, which must be investigated for safety and profit reasons. It has traditionally been considered that constraints on process states can be added to EMPC design to prevent the controller from computing control actions which create problematic operating conditions for process equipment. However, no rigorous investigation has yet been performed to analyze whether, when a …

An Energy Profile Model For Fused Deposition Modeling 3d Printing Process, Calvin Hawkins

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

This project develops a strategy to monitor and estimate the energy consumption of fused deposition modeling (FDM) additive manufacturing, which will benefit manufacturers and designers seeking to design and manufacture products with minimal energy consumption.

Understanding The Impact Of Virtual-Mirroring Based Learning On Collaboration In A Data And Analytics Function: A Resilience Perspective, Nabil Raad

Wayne State University Dissertations

Large multinational organizations are struggling to adapt and innovate in the face of increasing turbulence, uncertainty, and complexity. The lack of adaptive capacity is one of the major risks facing such organizations as the rapid change in technology, urbanization, socio-economic trends, and regulations continues to accelerate and outpace their ability to adapt. This is a resilience problem that organizations are addressing by investing in Data and Analytics to improve their innovation and competitive capabilities. However, Data and Analytics projects are more likely to fail than to succeed. Competing on data and analytics is not only a technical challenge but also …

Understanding The Relationship Of Innovation And Quality In A Fast-Changing Market: An Automotive Industry Perspective, Donna Leanne Bell

Wayne State University Dissertations

In a time when the consumer electronics industry is getting new products to market at a rapid rate, automotive original equipment manufacturers (OEM) must identify ways of getting new products and features to customers faster and with high quality to maintain or increase market share. This accelerated product development process requires a positive relationship between conceptual design and quality in order for a firm to have high performance in strategic areas innovation and quality. The purpose of this dissertation is to research the impact that quality practices have on the advanced product development process. Specifically, this research is focused on …

Deep Learning Based Reliability Models For High Dimensional Data, Mohammad Aminisharifabad

Wayne State University Dissertations

The reliability estimation of products has crucial applications in various industries, particularly in current competitive markets, as it has high economic impacts. Hence, reliability analysis and failure prediction are receiving increasing attention. Reliability models based on lifetime data have been developed for different modern applications. These models are able to predict failure by incorporating the influence of covariates on time-to-failure. The covariates are factors that affect the subjects’ lifetime.

Modern technologies generate covariates which can be utilized to improve failure time prediction. However, there are several challenges to incorporate the covariates into reliability models. First, the covariates generally are high …

A Structured Methodology For Tailoring And Deploying Lean Manufacturing Systems, Kenneth John Gembel Ii

Wayne State University Dissertations

The seminal works of Peter Drucker and James Womack in the 1990’s outlined the lean manufacturing practices of Toyota Motor Corporation (TMC) to become a world leader in manufacturing. These philosophies have since become the springboard for a significant paradigm shift in approaching manufacturing systems and how to leverage them to optimize operational practices and gain competitive advantage. While there is no shortage of literature touting the benefits of Lean Manufacturing Systems (LMS), there has been significant difficulty in effectively deploying them to obtain and sustain the performance that TMC has achieved.

This body of work provides a novel methodology …

Economic Model Predictive Control Design Via Nonlinear Model Identification, Laura Giuliani, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an …

Data-Based Nonlinear Model Identification In Economic Model Predictive Control, Laura Giuliani, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details …

Modular Product Architecture’S Decisions Support For Remanufacturing-Product Service System Synergy, Johnson Adebayo Fadeyi

Wayne State University Dissertations

Remanufacturing is identified as the most viable product end-of-life (EOL) management strategy. However, about 80% of manufactured products currently end up as wastes. Besides other benefits, the product service system (PSS) could curtail the main bottlenecks to remanufacturing namely quantity, quality, recovery time of used product, and negative perception of remanufactured products. Therefore, the integration of PSS and remanufacturing has been increasingly recommended as an enhanced product offering. However, an integration that is informed by mathematical analysis is missing. Meanwhile, the variables that bolster the performance of PSS and remanufacturing are substantially influenced by product development (PD) decisions. Among the …

Proactive Coordination In Healthcare Service Systems Through Near Real-Time Analytics, Seung Yup Lee

Wayne State University Dissertations

The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved 'work-flow' coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time "operational intelligence" of service networks that promote efficiency and quality of …

Reliability Analysis By Considering Steel Physical Properties, Wujun Si

Wayne State University Dissertations

Most customers today are pursuing engineering materials (e.g., steel) that not only can achieve their expected functions but also are highly reliable. As a result, reliability analysis of materials has been receiving increasing attention over the past few decades. Most existing studies in the reliability engineering field focus on developing model-based and data-driven approaches to analyze material reliability based on material failure data such as lifetime data and degradation data, without considering effects of material physical properties. Ignoring such effects may result in a biased estimation of material reliability, which in turn could incur higher operation or maintenance costs.

Recently, …

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare, Milad Zafar Nezhad

Wayne State University Dissertations

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. …

A Data-Driven And Mixed Methods Analysis Of Automotive Retail Operations Management, Mark Allen Colosimo

Wayne State University Dissertations

The importance of effective retail operations management has never been more significant. Our research aims to expand the understanding for efficiency and dynamics of franchise outlets within retail networks with a focus on sales performance and profitability. The focus and contribution is the development of an actionable data analytics driven process by which automotive dealerships (retail outlets) can be analyzed to identify areas of opportunity for improvement. In general, automotive dealerships aim to sell product to make a profit, the manufacturer of the product/brand desires to sell vehicles to make a profit, and the customer desires to find a suitable …

The Impact Of Machine Learning Algorithms On Benchmarking Process In Healthcare Service Delivery, Egbe-Etu Emmanuel Etu

Wayne State University Theses

Currently, organizations have adopted and implemented a variety of innovative quality management philosophies, approaches, and techniques to stay competitive in an ever-changing global economy. Benchmarking is one of such techniques deployed by organizations to stay competitive. The motivation for this research stems from a real-world problem being faced by hospitals in the healthcare industry who have amassed a ton of data and want to embark on benchmarking project to assess the performance of the emergency departments due to challenges faced with poor management of operations which has led to high patient boarding rates, high patient wait-times, poor quality service, low …