Graduate Department of Industrial and Manufacturing Engineering

FAMU—FSU College of Engineering

Website: https://eng.famu.fsu.edu/ime

Chair: Okoli; Professors: Awoniyi, Liang, Okoli, Zeng, Zhang; Associate Professors: Dickens, C. Park, Vanli, Wang, Yu; Assistant Professors: Li, Sun, Sweat; Research Faculty: Hao, J.G. Park; Teaching Faculty: Devine, Georgiadis, Gray, Gross, Taylor; Adjunct Instructor: Ferreiro, Gomez; Professor Emeritus: Braswell

The Department of Industrial and Manufacturing Engineering offers three graduate degree programs: the Master of Science (MS) and Doctor of Philosophy (PhD) in Industrial Engineering and the Master of Science (MS) in Systems Engineering (MSSE). MSSE is also offered as a fully online degree.

Industrial Engineering is a broad discipline that encompasses education and basic/applied research concerning the design, improvement, and installation of integrated systems of people, material, information, equipment, and energy. Graduate instruction and research are broadly grouped into three categories: manufacturing engineering, quality engineering, and industrial systems. Current research interests include manufacturing processes and systems; statistical; quality control; failure and life cycle analysis; mathematical optimization of complex production systems; condition monitoring; reliability engineering; statistical machine learning; distributed sensor networks; manufacturing process monitoring and diagnosis; set-covering theory; simulation environments; polymeric materials; nanomaterials processing and applications; additive thin-film manufacturing; printed electronics; carbon nanotube based functional materials; advanced composites and multi-scale materials; simulation for material processing; composite material processing.

Systems engineering (SE) is an interdisciplinary field of engineering that focuses on how to design and manage complex engineering systems over their life cycles. SE studies systems, processes, and practices required to develop them. These engineers are dedicated to ensuring all stakeholder needs are met in the best, most efficient way possible. SE facilitates deep integration of technical systems and helps ensure the systems developed are coherent, effective, and sustainable solutions to fulfill the system needs. SE professionals work with all facets of a system, from hardware to facilities, personnel to procedures. Our systems engineering program integrates engineering disciplines with industrial and management practices. Through the program, students will develop skills required in the national workforce for growing areas in the technology-driven global economy.

Research Facilities

The Department of Industrial and Manufacturing Engineering provides an excellent environment for instruction and research. The department has the following laboratories housed in the College of Engineering: Materials and Product Property Characterization, Computer Integrated Manufacturing, Precision Manufacturing, and Quality Engineering. In addition, the students have access to the 44,000 square foot state-of-the-art labs at Florida State University's High-Performance Materials Institute (see https://hpmi.research.fsu.edu/), which houses the following laboratories: Mechanical Testing Lab, Chemical and Thermal Analysis, Additive Manufacturing Lab, Manufacturing Lab, and Characterization Lab.

Each laboratory in the Department is equipped with state-of-the-art research and instructional equipment. Some of the available equipment in the labs are: 3D printers, Laser Scanner (Additive Manufacturing Lab); MTS Insight Testing System, MTS Landmark Servohydraulic System (Mechanical Testing Lab); Differential Scanning Calorimeter, Thermomechanical Analyzer (Chemical and Thermal Analysis); Scanning Electron Microscope, FTIR, UV-Vis, and Raman Spectroscopes, X-ray Differentiation and Scattering Machine, Atomic Force Microscope, Electromechanical and Electrochemical Testing Station (Materials and Product Property Characterization Lab); Twin Screw Extruder, Autoclave, Laser Cutting Machine, High-Power Sonicator, Temperature-Humidity Test Chamber (Manufacturing Lab).

Students have access to computer facilities, which includes both IBM-compatible PCs and high-performance engineering workstations. The Department offers access to a wide variety of software for CAD/CAM optimization, simulation, and statistical analysis, including Matlab, Minitab, Design Expert, R, Arena, and Simio computing environments. The Department also has access to the full Siemens PLM software suite for digital manufacturing, life cycle management, manufacturing operation management, and integrated solution for computer-aided design, manufacturing, and engineering. The statistical and simulation software and computing facilities are located in the Quality Engineering Lab. The solid modeling and CAD/CAM software are located in the Computer Integrated Manufacturing Lab. Technical support for software and hardware maintenance are provided by the Department and the College. In addition, the students have access to the Florida State University High Performance Computing (HPC) Cluster for intensive distributed-memory parallel computations.

Master of Science (MS)

The department offers a variety of Master of Science in Industrial Engineering (MSIE) program options to accommodate students' needs and specializations. Students may pursue a traditional MS or an MS with specialization in engineering management. The traditional MS program is research based, requiring the students to write and defend a thesis in their chosen area. The Master of Science with specialization in engineering management (MSIE-EM) does not require a thesis. The department also offers a BS-MS combined pathway, which provides students with a unique opportunity to complete graduate education on an accelerated schedule. Additionally, the department offers an MS in Systems Engineering (MSSE), a course-based, non-thesis degree designed for both full-time students and full-time working professionals. MSSE is also offered as a fully online program. The Industrial Engineering Graduate Handbook, which is available from the department, provides a complete description of all programs and requirements.

Admissions

Candidates for admission to graduate study in industrial engineering must meet university and departmental criteria. In some cases, students may be admitted on a provisional basis pending successful completion of prerequisite work. In all matters concerning admission, decisions made by the departmental graduate committee are final. Students who do not have a bachelor's degree in industrial engineering are required to complete the following prerequisite courses before undertaking graduate study:

• EGN 3443 Statistical Topics in Industrial Engineering (3)
• MAC 2313 Calculus with Analytic Geometry III (5) OR MAS 3105 Applied Linear Algebra (4) OR equivalent course as determined by the graduate committee.
• ESI 3312C Operations Research I: Deterministic (3) OR ESI 4313 Operations Research II: Nondeterministic (3) OR equivalent course as determined by the graduate committee
• a class in FORTRAN, PASCAL, C, or other modern programming language (required as evidence of proficiency in programming).

Admission Requirements for Traditional MSIE

• A BS in industrial engineering (or a related field) from an accredited college or university, with a GPA of at least 3.0 in all work attempted while registered as an upper-division undergraduate student working toward a baccalaureate degree
• Minimum scores of at least 155 on the quantitative portion and 146 on the verbal portion of the GRE
• A minimum score of 80 (iBT) on the TOEFL or a minimum of 6.5 on the IELTS (international students only)
• Three letters of recommendation, addressed to the Director of Graduate Studies, assessing the applicant's potential to do graduate work
• A statement of professional goals

Admission Requirements for MSIE with Specialization in Engineering Management

Requirements for admission to this program are identical to the MSIE admission requirements, except that, (1) GRE minimum score requirements are 151 in quantitative and 146 in verbal, and (2) applicants' BS degree can be in engineering, computer science, mathematics, physics, or a related area as determined by the Director of Graduate Studies.

The department also offers a BS-MS combined pathway toward MSIE-EM. Well-qualified students, who are expected to have a GPA of 3.3 or better in the undergraduate studies, are eligible to apply for the combined BS-MS pathway during the spring semester of their third year in the College. Qualified undergraduate students interested in the program should meet with the undergraduate academic advisor and the department graduate program director in the spring semester of their third year to determine whether they are eligible to apply and to plan their study in the senior year. Application to the undergraduate portion of the program will be reviewed by the department graduate committee and admission will be decided by the graduate program director with recommendation from the graduate committee and undergraduate academic advisor. Once admitted, students can proceed with taking 3 graduate level industrial engineering courses in their senior year to replace 8 credit hours in the existing undergraduate program. Admitted students should carry a course load of no more than fifteen (15) semester hours, and need to receive the approval of the dean, the department chair, and the undergraduate advisor prior to registration. Students will register the course at the graduate level and be graded as graduate students. Student will have the option to graduate with BS degree with the three graduate level courses.

For admission to the graduate portion of the program the students must make a formal graduate application in their senior year. Students who are interested in enrolling in the graduate program should meet with the director of graduate studies in the senior year to determine admission requirements and whether they are eligible to apply. Application, evaluation, and admission will follow the standard IME department requirements for MS in Engineering Management.

Admission Requirements for MS Systems Engineering

Requirements for admission to this program are identical to the MSIE admission requirements, except that, (1) GRE minimum score requirements are 151 in quantitative and 146 in verbal and (2) applicants' BS degree can be in engineering, computer science, mathematics, physics, or a related area as determined by the Director of Graduate Studies.

Degree Requirements

Thesis Option

Each MSIE student who intends to complete a thesis is required to take a minimum of thirty (30) semester hours (twenty-four semester hours of course work and six semester hours of thesis). At least eighteen semester hours of the course work hours must be taken in the Industrial and Manufacturing Engineering Department. Students must maintain an overall GPA of 3.0 or above in order to graduate.

When filing a degree plan, students must specify one of the department's areas of concentration as their major: manufacturing systems and engineering or quality engineering and industrial systems. If the desired area of concentration differs from the initial area assigned (based on the student's graduate application), a petition to the Director of Graduate Studies must be submitted requesting the change.

There are three sets of courses under the traditional MSIE program: core courses, specialization industrial engineering courses, and electives:

Core Courses. Every student choosing the thesis option must take the following courses and receive a grade of “B” or better in each: ESI 5408, Applied Optimization; ESI 5247, Engineering Experiments; ESI 5525, Modeling and Analysis of Manufacturing and Industrial Systems; and EIN 5936, Graduate Seminar.

Specialization Courses. These courses are used in defining minimum requirements for each specialization area. Each student is required to take at least three from those courses listed in his or her chosen area of specialization. Substitutions may be made with the approval of the student's advisory committee and the Director of Graduate Studies. Please refer to the departmental Website at https://www.eng.famu.fsu.edu/ime.

Electives. Elective courses provide program variation for students. An industrial engineering graduate course may be selected as an elective course. With the consent of the advisory committee, the student may take courses from other engineering departments or other academic schools or colleges at the two universities.

Non-Thesis Option

Under exceptional circumstances, students may be allowed into the MSIE non-thesis option. In such cases, students are required to complete a minimum of thirty (30) semester hours of course work at the graduate level, at least twenty-four of which must be taken in the Department of Industrial Engineering. Each student must obtain an overall GPA of 3.0 or above in order to graduate. Students should contact the department to learn more about specific course requirements for this program.

Specialization in Engineering Management

Students are expected to complete thirty semester hours of course work and will not be required to complete a thesis. Industrial Engineering Core courses constitute eighteen credit hours, Management core courses constitute three credit hours, and the elective courses constitute nine credit hours. At least three credit hours of the electives must be taken at the College of Engineering. Students must maintain a minimum GPA of 3.0 at all times while enrolled in the program in order to graduate. Students should contact the department to learn more about specific course requirements for this program.

Combined Pathway

All BS-MS students must take the following distribution of courses for a total of 30 credit hours to receive the combined BS-MS degree. Nine of the 30 credit hours must be taken during the senior year of the student's BS degree program as the shared credits. The remaining 21 credit hours are taken as part of the MS degree program. Students have the option to graduate with only the BS degree at the completion of the nine credit hours of the graduate courses.

MS Systems Engineering

Students are expected to complete 30 semester hours of course work. The program requires seven core courses and three technical electives. Students must maintain a minimum GPA of 3.0 at all times while enrolled in the program in order to graduate. Students should contact the department to learn more about specific course requirements for this program. MSSE is offered as both traditional and fully online program.

Doctor of Philosophy (PhD)

The PhD in industrial engineering is designed for students and professionals who wish to pursue academic careers or to achieve advanced standing in the field. The general requirement is a minimum of 45 semester hours of work beyond the baccalaureate degree, excluding any credits earned for a master's degree thesis, or a minimum of 33 semester hours beyond the master's degree.

Typically, 12 of the 45 semester hours will have been satisfied by a student who has earned a master's degree in industrial engineering, or a closely related field. Of the remaining required hours, nine must be letter-graded course work combined with a minimum of twenty-four additional hours of dissertation research. The course work beyond the master's consists of: 1) eighteen semester hours of breadth-requirement core courses and 2) up to six or more semester hours of depth-requirement courses, as determined by the student's doctoral supervisory committee. Residency and time-for-completion requirements are determined by the student's university of enrollment. Students must maintain a minimum GPA of 3.4 at all times while enrolled in the program. Students must also pass several milestone examinations as detailed in The Industrial Engineering Graduate Handbook. Doctoral candidates must meet the department publication requirements before the viva voce of their dissertation.

Admissions

Note: The following standards also pertain to students who wish to pursue a PhD but have not yet obtained their master's degree.

Applicants must meet the following minimum requirements:

1. Have a baccalaureate or master's degree in industrial engineering (or related field) from an accredited college or university, with a grade point average (GPA) of at least 3.0 on a 4.0 scale, and at least 3.4 GPA on master's degree work
2. Have a minimum score of 155 on the Quantitative portion and 150 on the Verbal portion of the GRE
3. Have a minimum score of 80 on the TOEFL iBT (580 paper based) or a minimum of 6.5 on the IELTS (international students only)
4. Three letters of recommendation, addressed to the Director of Graduate Studies, assessing the applicant's potential to do graduate work
5. A statement of professional goals

Core Courses for PhD Students

All PhD students are required to take the following courses as soon as possible after their admission to the PhD program. These courses provide students with a common, solid background in mathematics, statistics, and industrial engineering.

During the first calendar year of the PhD program, students must select a single course from each of the Mathematics and Computational course groups and must earn a grade of “B” or higher. Students who do not satisfy this requirement may be dismissed from the program.

Mathematics Course Group

MAA 5306 Advanced Calculus I (3)

MAP 5345 Elementary Partial Differential Equations I (3)

STA 5323 Introduction to Mathematical Statistics (3)

Computational Course Group

EIN 5930r Specialized Topics in Industrial Engineering (1–6)

MAD 5403 Foundations of Computational Methods I (3)

MAP 5395 Finite Element Methods (3)

Or

EIN 5930 Special Topics in Industrial Engineering (1–6)

Note: The required topic is “Finite Elements Methods” for three (3) credit hours.

STA 5106 Computational Methods in Statistics I (3)

IE Core Course Group

The following courses are required if the student did not take them to fulfill requirements for the master's degree: ESI 5247 Engineering Experiments; ESI 5408 Applied Optimization; ESI 5525 Modeling and Analysis of Manufacturing and Industrial Systems; EIN 5020 Research Methodology; and EIN 5936 Graduate Seminar.

Core courses cannot be taken on a pass/fail (S/U) basis.

Preliminary Examination

Following completion of the required coursework as defined in the degree plan, and upon certification of the doctoral supervisory committee that the student has 1) maintained a minimum 3.4 GPA and 2) progressed sufficiently in the study of industrial engineering and its research tools to begin independent research in the area of the proposed dissertation, the student is ready to take the preliminary examination.

The purpose of the preliminary examination is to test the adequacy of a student's background related to the student's area of concentration and to determine if the student is adequately prepared to formulate and undertake acceptable dissertation research. The procedures are available from the department.

Proposal and Dissertation

After completion of the preliminary examination, the student is admitted to formal candidacy for the PhD. After a period of preliminary research as determined by the doctoral committee, a research proposal must be successfully presented to the committee by the doctoral candidate.

The research proposal is a description of the research which the student intends to undertake, which will be reported in a detailed, comprehensive fashion in the completed dissertation. The research proposal must be submitted to the supervisory committee after the student passes the preliminary exam (usually one year after the preliminary exam) and before beginning dissertation research. The student must also provide an oral presentation to the committee at least one week after submitting the proposal. The proposal offers the student an opportunity to convince the supervisory committee of the appropriateness of the research topic, as well as of his/her capability to pursue the projected topic to a successful conclusion.

Subject to approval of the doctoral candidate's committee confirming the candidate's readiness to defend his/her dissertation, and upon meeting the department publication requirements, the candidate may proceed to defend their dissertation research. A doctoral dissertation then must be completed on a topic approved by the candidate's doctoral supervisory committee. To be acceptable, it must be an achievement in original research constituting a significant contribution to knowledge and representing substantial scholarly effort on the part of the student. The doctoral supervisory committee, department chairperson, and such other members of the faculty as appointed by the academic dean or specified by university regulations will conduct the examination. Publication of the dissertation shall conform to the regulations of the university in which the student is registered.

During the dissertation defense, all committee members and the student must be physically present. In cases where this is not possible, the department allows no more than one member to participate in the defense in real time via distance technology. The distance technology must allow two-way audio and visual links.

Definition of Prefixes

EEL—Engineering: Electrical

EIN—Industrial Engineering

EMA—Materials Engineering

EOC—Ocean Engineering

ESI—Industrial/Systems Engineering

PRO—Prosthetics/Orthotics

Graduate Courses

EEL 5606. Introduction to Mobile Robotics and Unmanned Systems (3). This course provides a thorough technical overview of autonomous vehicles for engineering students interested in understanding the basics of unmanned systems. The principles and methodology involved for the systems development is discussed. The course uses practical examples of developing autonomous unmanned vehicle systems.

EEL 5688. Principles of Autonomous Systems (3). Prerequisite: EEL 5605. This course provides an in-depth review of the principles of autonomy by reviewing probability theory and covering topics in pattern recognition, computer vision/perception, localization/SLAM, planning, and unsupervised/supervised learning.

EGN 5444. Big Data Analytics in Engineering (3). Prerequisites: EGN 3443. This course introduces the fundamentals of big data analytics, including data loading, cleaning, transformation, visualization, predictive analytics and data-driven decision making, with an emphases on computer implementation and engineering applications.

EIN 5020. Research Methodology (3). This course provides a structured and easily understandable step-by-step approach for students to learn the key components that compromise a sound research process.

EIN 5182. Engineering Management (3). This course introduces the principles, tools, and techniques of modern engineering management using the framework of the product/project life cycle.

EIN 5184. Systems Engineering Leadership (3). This course provides systems engineers with effective and tailored leadership training to successfully develop and lead multi-disciplinary teams throughout a system's life cycle.

EIN 5328. Environmentally Conscious Design and Manufacturing (3). Prerequisite: Graduate standing. This course offers a review of basic concepts and fundamentals of environmentally conscious design and manufacturing. The topics include ecology and environment; review of environmental laws and regulations pertaining to design and manufacturing; the global picture of environmental concerns; integration of environmentally conscious design and manufacturing within a company; and life-cycle analysis for product and process design.

EIN 5353. Engineering Economic Analysis (3). Prerequisites: EGN 3443 and MAP 3305. This course includes feasibility science, mathematics and engineering focused on the engineering economic analysis of design and system alternatives for high technology operations.

EIN 5356C. Cost Estimating for Engineering Economic Analysis (3). Prerequisite: Instructor permission. This core course provides an improved understanding and application of engineering economics and cost analysis, which are critical in a Systems Engineer's toolkit. The course include cost aspects of systems engineering, exploring cost from a decision-making perspective.

EIN 5392. Manufacturing Processes and Systems (3). Prerequisite: EGN 4000. Material forming, material removal and material joining processes. Shop floor layout topics. Material flow topics. Information system topics. System integration topics. Manufacturing system evaluation topics. Case studies and design exercises.

EIN 5396. Materials by Design (3). Prerequisites: MAC 2313, MAS 3105, and PHY 2048C, or instructor permission. The interdisciplinary course focuses on the mechanics, software, science, principles, and practices of designing composite materials. Students explore advanced concepts in materials science, applying theoretical knowledge and innovative design principles to optimize material properties and performance in various engineering applications.

EIN 5445C. Technology Entrepreneurship and Commercialization (3). This course simulates, in an academic environment, the process of creating and analyzing business models and commercialization plans for technology-based products or services.

EIN 5622. Computer-Aided Manufacturing (3). Prerequisite: EIN 3390C. CAD/CAM. Numerical Control (NC) and Computer Numerical Control (CNC). Programmable automation. Computer-aided process planning.

EIN 5905r. Directed Individual Study (1–3). (S/U grade only). Prerequisite: Instructor permission. May be repeated to a maximum of six semester hours.

EIN 5930r. Special Topics in Industrial Engineering (3–6). This course discusses topics in industrial engineering with emphasis on recent developments. Topics and credits vary; consult the instructor. May be repeated to a maximum of twelve (12) credit hours; repeatable within the same term.

EIN 5931. Leadership and Communications (3). Prerequisites: Graduate standing and EGN 3613. Course topics include leadership theories, motivation, goal setting, planning, proposal writing and technical presentations. Presentations given by business leaders are planned.

EIN 5936r. Graduate Seminar (0). (S/U grade only). Research presentations by faculty, students, and guests from industry.

EIN 6901r. Master's Thesis (1–12). (S/U grade only). Prerequisite: Approval by department. This course provides a means of registering for thesis research work and recording progress towards its completion. May be repeated to a maximum of forty-five (45) credit hours; repeatable within the same term.

EIN 8976r. Master's Thesis Defense (0). (P/F grade only.)

EMA 5015C. Nanomaterials and Nanotechnology (3). This course is designed to provide students the basic understanding and up-to-date knowledge on nanostructured materials, characterization methods, nano-devices, and nano-fabrication through class lectures, literature reading, and hands-on lab experiments.

EMA 5182. Composite Materials Engineering (3). Prerequisite: Instructor permission. Course provides basic understanding of composite materials. Topics include introduction to composite materials, properties and forms of constituent materials, consideration of composite behavior and failure modes, characterization of material performance and testing, introduction to available manufacturing techniques, laboratory demonstrations, and case studies.

EOC 5518. Marine Vehicles Engineering Principles (3). This course provides a thorough technical overview of naval architecture of advanced marine vehicles. As an introduction to naval architecture and marine vehicles, this course provides the practicing systems engineer the basic knowledge and skills necessary to lead a team of engineers with marine vehicles as part of the mission and project.

EOC 5519. Marine Systems Engineering Principles (3). In this course, students apply strategic and critical thinking principles to the development of marine systems, and develop a comprehensive approach to the integration of hull, propulsion, and mission systems into marine vehicle design.

ESI 5000. Design Considerations for Systems Engineering (3). This course provides students with knowledge and practical experience in quality and reliability measures for systems engineering. The course covers principles of Failure Mode and Effects Analysis (FMEA), reliability specifications, design for reliability, human centered design, accelerated testing, mechanical stress and analysis, software reliability, cybersecurity, supplier reliability, mathematical and statistical models for process control, life distributions and concepts, design for quality, focus on customers, six sigma, total quality, and the importance of quality in design.

ESI 5001. Systems Test and Evaluation (3). This course provides students with knowledge and practical experience in system test and evaluation (T&E) as practicing systems engineers and discusses how tests are defined, designed, and conducted; it examines how data from the tests are evaluated against the system requirements. Test and evaluation techniques of system design and performance are analyzed throughout the course, and the feedback loop of data analysis is introduced to identify the need for design changes to improve safety, correct failures, verify supportability of the systems, and support investment decisions.

ESI 5228. Introduction to ISO 9000 (3). Prerequisite: Instructor permission. This course utilizes case studies and demonstrations to introduce students to the ISO 9000 quality system standards, quality auditing, audit report writing, and documenting the requirements.

ESI 5243. Engineering Data Analysis (3). Prerequisite: EGN 3443 and instructor permission. This course provides students with an understanding of the methods for the analysis of data from engineering systems, and it focuses on empirical model building using observational data for characterization, estimation, inference, and prediction of engineering systems.

ESI 5247. Engineering Experiments (3). Prerequisites: EGN 3443, ESI 5243. This course introduces designing experiments and analyzing the results. It is intended for engineers and scientists who perform experiments or serve as advisors to experimentation in industrial settings. Students must have an understanding of basic statistical concepts. A statistical approach to designing and analyzing experiments is provided as a means to efficiently study and comprehend the underlying process being evaluated. Insight is gained that leads to improved performance and quality.

ESI 5249. Response Surfaces and Process Optimization (3). Prerequisite: ESI 5247. This course explores combined statistical experiment designs, empirical model building, and optimization methods. Topics include restrictions on randomization, mixture experiments, and robust design. Emphasis is placed on software tools to build designs and perform appropriate analyses.

ESI 5324. Managing Supply Chains for Resilience (3). Prerequisite: ESI 3312C. This course covers key concepts, models, and analytical tools of supply chain management, including facility location, supply-chain network design, aggregate planning, inventory management, risk-pooling strategies, product-design strategies for supply-chain management, distribution strategies, the bullwhip effect, and distribution management.

ESI 5353. Engineering Risk Analysis and Decision Making with Uncertainty (3). This course provides students with the knowledge and practical experience in risk analysis, risk identification, risk mitigation strategy development, ethics in risk management, communicating uncertainties, and risk leadership in complex organizations. Stochastic modeling and probabilistic theoretical models are exercised, and students are expected to understand probability basics.

ESI 5408. Applied Optimization (3). Prerequisite: ESI 3312C or equivalent. This course examines optimization topics relevant to industrial operations and systems. Emphasis on basic modeling assumptions and procedure implementation.

ESI 5440. Integer Programming (3). Prerequisite: Instructor permission. This course is designed to equip students with the necessary skills to discover the unique underlying structure of an optimization problem, analyze the polyhedral characteristics of IP formulations, develop good IP reformulations, and design rigorous solution approaches which can efficiently solve the problem.

ESI 5451. Project Analysis and Design (3). Prerequisites: EGN 3613 and ESI 3312C. This course focuses on project analysis and evaluation, utilizing networks and graph theory, advanced engineering economy, simulation procedures and other evaluation software. Project implementation topics include resource shortfalls and expediting. Students consider case studies and design exercises.

ESI 5510. Fundamentals of Systems Engineering (3). This course provides students with a fundamental understanding of Systems Engineering (SE). The course introduces multidisciplinary SE technical processes over the life cycle of a system including growing a deep awareness and understanding of analyzing and documenting user needs.

ESI 5512. System Requirements Analysis and Knowledge Management (3). The course provides students with the knowledge and practical experience in system requirement development and analysis as practicing systems engineers. The course introduces key knowledge management principles and practices along with a thorough understanding of methods to codify intellectual property and tacit knowledge into explicit knowledge for the betterment of the organization and the system people, processes, and products supported.

ESI 5522. Complex Systems Modeling and Simulation (3). Prerequisite: Graduate standing. ESI 5510 is recommended. This course prepares students to propose, develop, validate, and utilize small and large scale simulations to specific problems in systems engineering.

ESI 5525. Modeling and Analysis of Manufacturing and Industrial Systems (3). Prerequisites: EIN 4333, ESI 4523, and instructor permission. This course discusses the modeling and analysis of material flow systems, flow-shop and job-shop scheduling, material handling system analysis, mathematical and simulation modeling for general manufacturing and industrial systems.

ESI 5536. Model Based Systems Engineering and Simulation (3). This course provides students with knowledge and practical experience in Model Based Systems Engineering (MBSE) and simulation. The International Council on Systems Engineering (INCOSE) defines MBSE as the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. The course introduces de facto industry standard MBSE modeling software and tools, and their use in the design and optimization of systems.

ESI 5554. Systems Engineering Principles for Aerospace. (3). Prerequisites: Graduate standing academic success; ESI 5510 is recommended. This is an elective course of the Master of Science in Systems Engineering (MSSE) curriculum, designed to provide the student with a fundamental understanding of systems engineering as applied to aerospace systems including aircraft and spacecraft.

ESI 5556. Digital Transformation for Systems Engineering. (3). The goal of this course is to educate system engineers on the fundamentals of data science, with a focus on the process for developing and executing successful engineering projects that involve the collection, analysis, and modelling of empirical data. All lessons flow from the systems engineering and data science life-cycle framework. It is preferred, but not required, for students to have already taken ESI 5110 and ESI 5563.

ESI 5590. Human Factors for Systems Engineering (3). This is an elective course of the Master of Science in Systems Engineering (MSSE) curriculum, providing a fundamental understanding of human characteristics, abilities, and limitations as well as their consideration in requirements and design of systems.

ESI 5617. Data-Driven Informatics for Intelligent Systems (3). Prerequisites: EGN 3443 and MAS 3105. This course introduces multi-dimensional data modeling and monitoring tools for predicting variational behaviors of industrial systems. Students learn how analytics help build intelligent systems to improve quality and productivity. Cutting edge research on analytics for Industrial Internet of things is also introduced.

ESI 5681. Deep Learning in Practice (3). Prerequisites: EGN 3443, ESI 3312C, and MAS 3105. This course introduces three main neural networks (ANN, CNN, and RNN) and the realization in Python. Students learn the basics such as forward propagation, backward propagation, and gradient descent algorithms, as well as up-to-date neural network projects like (YOLO, VGG19, Resnet50, etc.)

ESI 5685. Introduction to Machine Learning (3). Prerequisites: EGN 3443, ESI 3312, and MAS 3105, or instructor permission. This course is an introductory course to machine learning, aiming at advanced undergraduates or first-year graduate students.

ESI 5705. Cybersecurity for Systems Engineering (3). This course provides an overview and systems level understanding of applying cyber security principles and best practices to the system engineering process throughout the entire lifecycle of a system. This course prepares systems engineers to manage cyber risks in a preemptive and proactive manner encouraged by systems engineering.

Doctoral

EIN 6980r. Dissertation (1–12). (S/U grade only). Prerequisite: Admission to doctoral candidacy. This is a mandatory class for all PhD seeking students. May be repeated to a maximum of forty-eight (48) semester hours within the same term.

EIN 8964. Preliminary Doctoral Examination (0). (P/F grade only.) Prerequisite: Doctoral candidate standing.

EIN 8985r. Dissertation Defense (0). (P/F grade only.) Prerequisite: Doctoral candidate standing.