Operations Research, Systems Engineering and Industrial Engineering Commons^™

Spatial Markov Equilibrium Models For Taxi Services: Driver Decision, Search Friction, And Locational Pricing, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper develops a modeling framework for stochastic multi-agent systems and applies it to equilibrium and pricing analysis in urban taxi markets. Travel demand is represented as a trip network and embedded in a Markov chain that captures both locational and in transit taxi states, with transition dynamics reflecting trip durations, search frictions, spatial competition, and drivers’ perceptions of long-term value. The framework features a parametric Markov chain with endogenous transition probabilities and a behavioral model in which agents’ decisions depend on anticipated long-term rewards. We establish equilibrium existence and examine two locational pricing schemes that align individual incentives with …

Nonconvex Optimization Methods Under Inexact Information, Dat Ba Tran

Wayne State University Dissertations

This thesis focuses on the design and convergence analysis of algorithms for solving nonconvex optimization problems under inexact first-order information. We introduce Inexact Reduced Gradient (IRG) methods for general smooth functions and Inexact Gradient Descent (IGD) methods for $\mathcal{C}^{1,1}_L$ functions with relative and absolute errors. Additionally, we develop Inexact Proximal Point and Inexact Proximal Gradient methods for weakly convex functions. Our methods improve the performance of standard inexact proximal point methods, inexact proximal gradient methods, and inexact augmented Lagrangian methods by approximately 2.5 to 10 times in terms of iteration complexity for image processing tasks. Moreover, we propose new derivative-free …

Routing Battery-Constrained Delivery Drones In A Depot Network: A Business Model And Its Optimization-Simulation Assessment, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper proposes a novel business model for on-demand package shipment services using drones, and evaluates different modeling and solution approaches for the drone routing problem that underpins the service operation. In the proposed service, customers’ shipment orders of arbitrary origins and destinations, payload weights and bid values are collected every five minutes, and available drones from multiple depots are then dispatched to fulfill a subset of these orders in a way to maximize profit. A drone path starts from a depot, serves one or more customer orders in sequence, and ends at a depot for battery recharging, which incurs …

Product Development Resilience Through Innovation Management And Agile Efficiency Design, Anna Nguyen

Wayne State University Dissertations

ABSTRACT

PRODUCT DEVELOPMENT RESILIENCE THROUGH INNOVATION MANAGEMENT AND AGILE EFFICIENCY DESIGN

by

ANNA NGUYEN

December 2023

Advisor: Dr. Kai YangMajor: Industrial Engineering Degree: Doctor of Philosophy In recent years there have been constantly changing innovation technology, economic inflation, and material costs considering the various headwinds that the Automotive Industry faced. To adapt to these rapid changes or under non-standard timing circumstances, Auto Parts Manufacturing focuses on technically advanced design-thinking and engineering competency but has limited the manufacturers’ visibility of new product development and control of operations on a global level. Mass production firms focus on identifying new technology but more …

Managing The Success Of Tpm Deployment & Sustainment In The Automotive Industry, Kevin Gembel

Wayne State University Dissertations

Maintenance has assumed increased importance in manufacturing as companies look for ways to gain an advantage over their competitors. The philosophical benefits of jointly implementing Total Productive Maintenance (TPM) programs in concert with other lean and quality programs have been discussed in the manufacturing literature. However, there is an absence of real-life implementation studies for objective assessment. To the best of our knowledge, this is the very first longitudinal study to investigate the implementation of TPM programs at a large global OEM across several regions and facilities using objective data collected across all key functions and outcomes. We study a …

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 …

Data-Driven Semantic Modeling For Welding Assemblies, Fahim Ahmed

Wayne State University Dissertations

A significant information gap is prevalent between the design domain and the manufacturing domain. The designers lack manufacturing awareness as they have little knowledge regarding the manufacturability of their designs. The welding domain falls prey to this issue as designers lack manufacturing awareness and welding engineers lack weldability of the product assembly. Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making or weldability prediction for Resistance Spot Welding (RSW) as well. We have used machine …

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 …

Automotive Product Assortment Planning With Consideration Of Distribution Channel Dynamics, Madagedara Maduka Rushanjalee Balasooriya

Wayne State University Dissertations

Automotive original equipment manufacturers (OEMs) are putting a lot of effort into maintaining an efficient order catalog to offer better products to their customers in maketo-stock (MTS) markets. While product “assortment planning" research grows to more effectively identify the best assortments for OEMs, the existing configurable assortment planning literature ignores a significant dimension: the impact of distribution channels, especially dealer franchise networks. Dealers face unique challenges in trying to best satisfy the choice preferences of their local consumers by balancing their limited product configuration inventory with profitability. In many predominantly MTS automotive markets such as the U.S., the reality is …

Improving Or Operations Using Machine Learning Techniques, Tannaz Khaleghi

Wayne State University Dissertations

Recently, health care related studies are being widely conducted by researchers using unique and efficient techniques to increase system profitability, quality of care, and patient satisfaction. Surgery department is considered as the hospital's engine, and cost of surgical services has a huge impact on the overall profitability of the hospital. This thesis proposes novel approaches to improve the efficiency of surgical services by using machine learning concepts.

In the first part, this research investigates the prediction of the surgery durations and Current Procedural Terminology (CPT) Codes. Accurate prediction of the surgery duration will improve the utilization of indispensable surgical resources …

Acute Coronary Syndrome Prediction: A Data-Driven Machine Learning Modeling Approach In Emergency Care, Joshua Oluwatobiloba Emakhu

Wayne State University Dissertations

Healthcare facilities are faced with significant challenges all year round, with patients presenting to the emergency department (ED) with different health issues. Of these challenges, heart disease seems to be an outlier. With heart disease being the primary cause of mortality and morbidity in both developed and developing countries, clinical concerns for acute coronary syndrome (ACS) are one of emergency medicine’s most common patient encounters. Of the three sub-categories of ACS, non-ST-segment elevation myocardial infarction (NSTEMI) has a long-term impact on the well-being of patients if left untreated. Previous efforts in hospital management have applied machine learning algorithms in differentiating …

A Combined Additive - Deformation - Machining (Adm) Manufacturing Process With Controlled Microstructures, Ahmed Nabil Taher Elalem

Wayne State University Dissertations

ABSTRACTA COMBINED ADDITIVE - DEFORMATION - MACHINING (ADM) MANUFACTURING PROCESS WITH CONTROLLED MICROSTRUCTURES by AHMED NABIL TAHER ELALEM August 2022

Advisor: Dr. Xin WuMajor: Mechanical Engineering Degree: Doctor of Philosophy

Direct metal additive manufacturing provides great flexibility in 3D shaping compared to traditional manufacturing processes of metal casting, deformation followed by machining and joining for obtaining the final production shape. On the other hand, it suffered from its dendric microstructure produced during solidification. This study reports the development of new combined metal additive and deformation processes to take advantage and reduce/eliminate the drawback of individual processes. The heating source mainly …

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 …

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 …

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 …

A Multi-Agent Semi-Cooperative Unmanned Air Traffic Management Model With Separation Assurance, Yanchao Liu

Industrial and Systems Engineering Faculty Research Publications

This paper presents an air traffic management framework to enable multiple fleets of unmanned aerial vehicles to traverse dense, omni-directional air traffic safely and efficiently. The main challenge addressed here is separation assurance in the absence of full coordination and communication. In this framework, each fleet is independently managed by a routing agent, which progressively plans the non-overlapping move-ahead corridors for vehicles in the fleet by solving a nonlinear optimization model. The model is artfully designed so that agents of different fleets need not engage in complicated multilateral communications or make guesses about external vehicles’ flight intents to maintain effective …

Methods To Integrate Domain Expert User Knowledge Into Process Discovery, Jasim Alnahas

Wayne State University Dissertations

Process Mining (PM) is a new era in business development management that reinforces business process sustainability. Process Mining uses various techniques to discover the process model and identify the root cause analysis of process delays based on the event log. There are three main types of process models: procedural models, declarative models, and hybrid models. Procedural models tend to discover the main pattern of the activity flows in the process. In contrast, declarative models analyze the process behavior and express this behavior as a compact set of rules between pair of two activities. Hybrid models are a combination of procedural …

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 …

Predicting The Geometrical Disassembly Feasibility Of Mechanical Assemblies In Design Phase, Header Alrufaifi

Wayne State University Dissertations

Disassembly of products has gained more and more attention due to the economic, environmental, and social benefits and the contribution to the protection of natural resources [1]. Where, disassembly is the first and usually the most critical and challenging process in most recovery processes (i.e. remanufacturing, reuse, maintenance, and recycling processes) which are essential reverse flows in circular economy systems. As policies, regulations, products, and systems move towards and strive for a circular economy, it is increasingly vital that disassembly analysis, models, and methods are feasible during development and manufacturing life-cycle stages. However, checking disassembly feasibility is considered a critical …

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 …

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, …

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 …