Industrial EngineeringResearch Facilities
Master of Science (MSIE)
Degree Requirements - Thesis Option
Degree Requirements - Non-Thesis OptionSpecialization in Engineering ManagementPh.D. Degree Admission Requirements Core Courses for Ph.D. StudentsPreliminary ExaminationDissertation Graduate Courses
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 studies in the Department of Industrial Engineering are broadly organized into the following two areas: manufacturing engineering and industrial systems. Current research interests include integrated product/process design, artificial intelligence in engineering, precision machining and metrology, intelligent processing of composite materials, predictive maintenance, manufacturing system analysis, rapid prototyping, set-covering theory, and quality control and management.
The Department of Industrial Engineering offers two graduate degree programs: Master of Science (MS) and Doctor of Philosophy (Ph.D.). 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 integrated products, manufacturing processes and system design; CAD/CAM; robotics; artificial intelligence in engineering; precision matching and metrology; rapid prototyping; composite material processing; quality control; quality engineering; manufacturing systems analysis; set-covering theory; simulation environments; supply chain management; and engineering management.
Faculty Teaching and Research Interests
Zhang, Chuck, Ph.D., Professor and Chairman, University of Iowa, Precision Machining and Metrology, Intelligent Processing of Composites Materials.
Awoniyi, Samuel A., Ph.D., Professor, Cornell University, Applied Optimization.
Liang, Richard, Ph.D., Professor, Beijing University of Aeronautics and Astronautics, Composite Materials, Polymer Materials, Property Characteristic Modeling.
Liu,Tao, Ph.D., Assistant Professor, Georgia Institute of Technology, Carbon Nanotube-Based Functional Materials, Processing-Structure-Property Relationship of Polymer and Polymer Nanocomposites, Non-Destructive Optical Characterization Techniques.
Okoli, Okenwa I., Ph.D., Associate Professor Director of Graduate Studies, University of Warwick, Composites Manufacturing, Structural Health Monitoring, Toughened Ceramics.
Owusu,Yaw A., Ph.D., Associate Professor, Pennsylvania State University, Manufacturing Processes and Materials, Rapid Prototyping.
Pignatiello, Joseph J., Jr., Ph.D., Associate Professor, Ohio State University, Quality Engineering, Applied Statistics.
Wang, Ben, Ph.D., Professor, Pennsylvania State University, Integrated Product & Process Design, Predictive Maintenance.
Zeng, Changchun, Ph.D., Assistant Professor, The Ohio State University, Polymeric Materials, Composite Materials, Nanomaterials.
Zhang, Mei, Ph.D., Associate Professor, Osaka Prefecture University, Nanomaterials Processing and Applications.
The Department of Industrial Engineering provides an excellent environment for instruction and research. The department has eight laboratories: Advanced Materials Processing, Applied Robotics, Optimization and Simulation, Composite Manufacturing and Testing, Computer Integrated Manufacturing, Automated Systems, Quality Engineering, Precision Manufacturing and Ergonomics. Each lab is equipped with state-of-the-art research and instructional equipment.
Students have access to computer facilities, which include SUN workstations and servers, IBM-compatible Pentium-based PC's and high performance engineering workstations. The department offers access to a wide variety of software, including CAD/CAM simulation, optimization and database management programs. Software development environments supporting research activities are maintained. In addition, the College of Engineering computing facilities support a SUN cluster with 15 Ultra Sparc Systems and LAN Manager environment.
Master of Science (MSIE)
The department offers master of science in industrial engineering (MSIE) program options to accommodate students' needs and specializations. Students may pursue a traditional MSIE or an MSIE with specialization in engineering management. For the traditional MSIE program, students are allowed to choose a thesis or non-thesis option. The specialization in engineering management does not require a thesis.
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
• ESI 3312 Operations Research I or ESI 4313 Operations Research II or an equivalent course (as determined by the graduate committee),
• Calculus III or Engineering Math or Linear Algebra or an equivalent course (as determined by the graduate committee), and
• A class in FORTRAN, PASCAL, or C (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 on all upper-division work;
• Good standing in the institution of higher learning last attended;
• A minimum combined score of 1050 on the verbal (400) and quantitative (650) portions of the GRE;
• A minimum score of 580 on the TOEFL (international students only);
• Three letters of recommendation, addressed to the Director of Graduate Studies, assessing the applicant's potential to do graduate work; and
• 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 applicant’s B.S. 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, which consists of twenty-four (24) semester hours of course work and six (6) semester hours of thesis. At least eighteen (18) semester hours of the course work hours must be taken in the industrial engineering department.
When filing a degree plan, students must specify one of the department's areas of concentration as their major: manufacturing engineering, quality engineering or industrial systems. The traditional MSIE program consists of three sets of courses: core course, specialization industrial engineering courses and electives.
Every student with the thesis option must take the following courses: ESI 5408: Applied Optimization; ESI 5247: Engineering Experiments; ESI 5525: Modeling and Analysis of Manufacturing and Industrial Systems; and EIN 5936: Graduate Seminar.
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.
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.
Degree Requirements - Non-Thesis Option
Students are required to complete a minimum of thirty-three (33) semester hours of course work at the graduate level, at least twenty-four (24) of which must be taken in the industrial engineering department. The following are the core courses for the non-thesis option:
EIN 5412: Computer-aided Manufacturing (3)
ESI 5247: Engineering Experiments (3)
ESI 5525: Modeling and Analysis of Manufacturing and Industrial Systems (3)
EIN 5936: Graduate Seminar (0)
ESI 5408: Applied Optimization (3)
ESI 5417: Engineering Data Analysis (3)
ESI 5451: Project Analysis and Design (3)
ESI 5154: Statistical Process Control (3)
ESI 5228: Introduction to ISO 9000 (3)
Specialization in Engineering Management
Students are expected to complete thirty-three (33) semester hours of course work and will not complete a thesis. Students should contact the department to learn more about specific course requirements for this program.
For further information, consult the department website at www.ie.eng.fsu.edu.
Doctor of Philosophy (Ph.D.)
Ph.D. Degree Admission Requirements
The Ph.D. 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 seventy-two (72) semester hours of work beyond the baccalaureate degree, excluding any credits earned for a master's degree thesis, or a minimum of forty-eight (48) semester hours beyond the master's degree.
Typically, twenty-four (24) of the seventy-two (72) 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, twenty-four (24) must be letter-graded course work combined with a minimum of twenty-four (24) additional hours of dissertation research. The course work beyond the master's consists of: 1) eighteen (18) semester hours of breadth-requirement core courses, and 2) six (6) or more semester hours of depth-requirement courses as determined by the student's doctoral supervisory committee.
• M.S. Degree in Industrial Engineering, science or mathematics, with
GPA of at least 3.4/4.0
• GRE scores: at least 700 Quantitative; at least 450 Verbal
• TOEFL score at least 580
• Three favorable recommendations
Core Courses for Ph.D. Students
All Ph.D. students are required to take the following courses as soon as possible after their admission to the program. These courses are required in order to provide students with a common, solid background in mathematics, statistics, and industrial engineering.
Following completion of a major portion of the course work, 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 to begin independent research for the proposed dissertation, the student will take a preliminary examination. The preliminary examination tests the adequacy of a student's background related to the student's area of concentration and determines if the student is adequately prepared to formulate and undertake acceptable dissertation research. The procedures are available from the department.
After completion of the preliminary examination, the student is admitted to formal candidacy for the Ph.D. A doctoral dissertation must be completed on a topic approved by the candidate's doctoral supervisory committee. The topic must be an achievement in original research constituting a significant contribution to knowledge and represent a 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.
For further information, consult the department website at www.ie.eng.fsu.edu.
Master of Science - Industrial Engineering Level Courses
EIN 5119C Computing Topics in Industrial Engineering (3) Prerequisite: COP 3221 or CGS 2402. State-of-the-art computing techniques for industrial engineers. Integration of structured programming, database management systems, mathematical analysis techniques, GUI interface languages and Internet networking principles. Design, development, debugging and management of complex computer-based projects.
EIN 5322 Engineering Management (3) Prerequisite: EIN 5353 (Engineering Economic Analysis). Modeling of existing and future organizations. Emphasis on organizations for the 21st century. Special consideration given to flat matrix models.
EIN 5336 Production Control (3) Prerequisite: ESI 5417. Basic concepts and fundamentals of production and operations analyses, planning and control. Topics include forecasting, aggregate planning, inventory control, materials requirements planning, operations scheduling, capacity management and case studies.
EIN 5353 Engineering Economic Analysis (3) Prerequisite: EIN 3443, MAC 3305. Feasibility science, mathematics and engineering focused on engineering economic analyses, specifically, design and system alternatives for high-technology operations.
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 5398 Manufacturing Materials Processing (3) Prerequisite: EIN 5392. Review of basic concepts and fundamental results of materials science. Fundamentals of casting processes and applications. Nontraditional methods in materials processing. Microscale material processing with applications to microelectronics and similar structures. Industrial byproduct processing. Automation issues. Case studies and design exercises.
EIN 5399 Concurrent Engineering (3) Prerequisite: Graduate or senior with instructor consent. Concurrent product and process design. Product life-cycle attributes. Design for manufacturing. Quality function deployment. Concurrent engineering project management topics. Case studies and design exercises.
EIN 5412 Computer-Aided Manufacturing (3) Prerequisite: EIN 4390C. CAD/CAM. Numerical Control (NC) and Computer Numerical Control (CNC). Programmable Automation. Computer-aided process planning.
EIN 5413 Computer-Aided Process Planning (3) Prerequisite: EIN 4390C, EIN 4312C, EGN 2123, CGS 3408. Role of process planning and computer-aided process planning (CAPP), development of CAPP, configuration of CAPP systems, input approaches of CAPP systems, process routing planning, machining operations design, variant CAPP systems, generative CAPP systems and artificial intelligence in CAPP.
EIN 5524 System Modeling and Simulation (3) Prerequisite: ESI 3443, FORTRAN. Discrete event, continuous and process simulation. Combined discrete/continuous simulation. Manufacturing systems modeling. Event graphs. Simulation languages and systems. Experimentation with models. Introduction to simulation-specific statistical problems. Model validation and verification issues. Design exercises.
EIN 5905r Directed Individual Study (1-3) (S/U grade only.) Prerequisite: Instructor consent. May be repeated to a maximum of 6 credit hours.
EIN 5930 Failure Analysis and Design for Reliability (3) Failure (yield and fracture) criteria with emphasis on design. Introduction to fracture mechanics and fatigue. Failure-mode analysis in metals, polymers and composites. Fractography, microscopy and nondestructive evaluation techniques. Introduction to reliability statistics and design approaches for reliability.
EIN 5930 Data Mining and OR Techniques (3) Instructs in appropriate methodology for processing and analyzing data. Instructs in finding analytical models and algorithms that would reveal internal structure and patterns of the data.
EIN 5930 Reliability Engineering (3) Prerequisite: ESI 5417. An introduction to reliability engineering. Applications of statistical methods for predicting product life using complete and censored data. Topics include degradation models, repairable systems, accelerated testing and failure-time regression models.
EIN 5930 Advanced Composite Engineering Seminar (3) Prerequisite: EMA 5182. Topics include modeling and simulation of composite fabrication processes, fiber preform/LCM process, high temperature composites, introduction to nanocomposites and multifunctional composites and affordability analysis.
EIN 5930 Nanomaterial and Nanotechnology (3) Prerequisite: instructor consent. Topics include introduction to fundamentals of nanomaterials and nanofabrication, nanoparticles and properties, nanoclay and nanotube nanocomposites, nano-biomaterials, nanoscale modeling and simulation, nanosensors and nanodevices, self-assembly and nano-manipulation.
EIN 5930r Special Topics in Industrial Engineering (1-6) Prerequisite: Instructor consent. Topics in industrial engineering with particular emphasis on recent developments. May be repeated to a maximum of 6 credit hours.
EIN 5931 Leadership and Communications (3) Prerequisite: Graduate standing. Topics include leadership theories, motivation, goal setting, planning, proposal writing and technical presentations. Presentations given by business leaders.
EIN 5936r Graduate Seminar (0) (S/U grade only). Research presentations by faculty, students and guests from industry.
EIN 6901r Master’s Thesis (1-6) (S/U grade only). Prerequisite: Approval by department. Each master's thesis shall be supervised by a Master’s degree supervisory committee. Completed Master’s thesis shall be presented to the department in the form of a written report and a seminar. May be repeated for a maximum of 9 credit hours.
EIN 8976r Master’s Thesis Defense (0) (S/U grade only.)
EMA 5182 Composite Materials Engineering (3) Prerequisite: Instructor consent. Covers basics for understanding 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.
ESI 5154 Statistical Process Control (3) Prerequisite: ESI 4234. Advanced methods of statistical process control for univariate and multivariate processes. Methods for change-point detection and estimation. Control chart performance comparisons. Process capability studies.
ESI 5228 Introduction to ISO 9000 (3) Prerequisite: Instructor consent. Introduction to ISO 9000 quality system standards. Quality auditing. Audit report writing. Documenting the requirements. Case studies and demonstrations.
ESI 5247 Engineering Experiments (3) Prerequisite: EIN 5417, EGN 3443. Introduction to designing experiments and analyzing their results. Intended for engineers and scientists who perform experiments or serve as advisors to experimentation in industrial settings. Students must understand 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 gained leads to improved product performance and quality.
ESI 5248 Environmentally Conscious Design and Manufacturing (3) Prerequisite: Graduate standing. Review of basic concepts and fundamentals of environmentally conscious design and manufacturing. 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.
ESI 5412 (FAMU)/ESI 5408 (FSU) Applied Optimization (3) Prerequisite: ESI 3312. Optimization topics relevant to industrial operations and systems. Emphasis on basic modeling assumptions and procedure implementation. Topics shall include linear programming, nonlinear programming, discrete optimization and large-scale optimization software. Design exercises. Please note: Students enrolled through FAMU should register for ESI 5412, while students enrolled in FSU should register for ESI 5408. (Please see:
ESI 5417 Engineering Data Analysis (3) Prerequisite: EIN 3443 or equivalent. Analysis of experimental and observational data from engineering systems. Focus on empirical model building using observational data for characterization, estimation, inference and prediction.
ESI 5451 Project Analysis and Design (3) Prerequisite: EGN 3613, ESI 3312. Project analysis and evaluation, utilizing networks and graph theory, advanced engineering economy, simulation procedures and other evaluation software. Project implementation topics including resource shortfalls and expediting. Case studies and design exercises.
ESI 5458 Optimization on Networks (3) Prerequisite: ESI 3312. Review of basic combinatorics. Basic concepts of graph theory. Matching and covering and applications. Traversability and path problems on networks and applications. Tree problems. Network flows and applications. Eulerian paths, Hamiltonian paths and applications. Location problems on networks. Design exercises.
ESI 5524 Advanced Simulation Applications (3. Prerequisite: ESI 4523 or EIN 5524. Application of simulation to complex systems including material-handling systems, real-time scheduling, high-speed/high-volume production, modern manufacturing techniques, healthcare delivery and logistics. Concurrent use of simulation and other analysis techniques. Use of experimental design, output analysis and validation techniques. Case studies.
ESI 5525 Modeling and Analysis of Manufacturing and Industrial Systems (3) Prerequisite: ESI 5412, ESI 5408, ESI 4523, ESI 3321C, EIN 4333. 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.
Ph.D. Level Courses
EIN 6126 Global Manufacturing Strategy (3) Prerequisite: EIN 5399, EIN 5524, EIN 5412. Global alliancing. Policy making. Manufacturing strategy. International standards.
EIN 6357 Advanced Engineering Economy (3) Prerequisite: EGN 3443, ESI 5451. Economic analysis of capital expenditure decisions. Financial mathematics and microeconomics. Decision under risk and uncertainty. Game theory and utility theory.
EIN 6396 Manufacturing System Analysis (3) Prerequisite: EIN 5524, ESI 5524. Review of manufacturing fundamentals, different approaches of system design, tools for analyzing manufacturing systems and metrics for evaluating manufacturing system performance. Case studies and design exercises.
EIN 6934 Total Quality Management Concepts (3) Prerequisite: Please consult with your advisor. This course is offered in conjunction with FEEDS.
EIN 6980r Dissertation (3-24) Prerequisite: Doctoral candidate standing. Mandatory class for all PhD seeking students. May be repeated to a maximum of forty-eight (48) semester hours.
EIN 8968 – Preliminary Doctoral Examination (0). (S/U grade only.) Prerequisite: Doctoral candidate standing.
ESI 6716r Advanced Topics in Optimization (1-9) Prerequisite: ESI 5408. Depending on the research interests of students and the instructor, this course shall cover advanced topics in optimization and their applications in industrial engineering. Each student shall produce a tutorial paper on an optimization problem/topic to be approved by the instructor.