Top Masters Programs in Industrial Engineering

The Industrial Engineering and Management Sciences PhD Program produces researchers who combine strength in core methodologies of operations research (e.g., optimization, stochastic modeling and simulation, statistics, and data analytics) with the ability to them to yield practical benefits in solving problems that are important in the real world. The program offers students the opportunity to use skills in computing, mathematical analysis and modeling, and economics to produce research that helps to improve the efficiency, quality, and the potential of organizations to fulfill their missions. The program prepares students for research-based careers in industry, academia, non-profit, and government.

Areas of specialization include Analytics, Stochastic Analysis Simulation, Optimization, Financial Engineering, Logistics Operations, Healthcare Engineering, Applied Statistics Statistical Learning, and Management Science.

Applicants must have a bachelor or master degree in a relevant discipline, but need not have a degree in industrial engineering. The program has attracted students of applied mathematics, computer science, economics, finance, mathematics, physics, statistics, and most other engineering disciplines.

See the Industrial Engineering and Management Sciences Degree Requirements for specific classes and procedures needed to complete this program.

The Master of Industrial Engineering degree is a graduate degree program administered by the George R. Brown School of Engineering, with the participation of the Rice University Departments of Mechanical Engineering and Statistics, and the Rice Center for Operations Research.

The program is designed to explore modern industrial systems, which arise in fields such as manufacturing, services, chain management, energy, transportation and healthcare. Analyzing and optimizing their performance is very challenging for example, the number of ways that Federal Express can route its vehicles vastly exceeds the number of atoms in the universe. These analyses are crucial their financial impact typically exceeds the profit margins in many industries, such as transportation and retailing.

To meet these challenges, the Master of Industrial Engineering degree emphasizes improving the quality and reliability of complex systems. It provides students with a deep set of analytical and engineering skills to make data-driven decision needed in every major economic sector. Graduates will help industry, governments, and non-profits improve efficiency in changing and uncertain environments.

Industrial Engineering does not currently offer an academic program at the level.

Master of Industrial Engineering (MIE) Degree Master of Business Administration (MBA) Degree.

Byrne, Psychological Sciences Fathi Ghorbel, Mechanical Engineering Illya V. Hicks, Computational Applied Mathematics C. Fred Higgs III, Mechanical Engineering Marcia K. O’Malley, Mechanical Engineering Amit Pazgal, Business Eduardo Salas, Psychological Sciences Andrew J. Schaefer, Computational Applied MathematicsLaura Schaefer, Mechanical Engineering Pol D. Spanos, Mechanical Engineering Richard A. Tapia, Computational Applied Mathematics Yin Zhang, Computational Applied Mathematics.

Leonardo Dueñas-Osorio, Civil and Environmental Engineering Philip A. Ernst, Statistics Philip T. Kortum, Psychological Sciences.

Matthew Brake, Mechanical Engineering Pedram Hassanzadeh, Mechanical Engineering.

For Rice University degree-granting programs: To view the list of official course offerings, please see Rice’s Course Catalog To view the most recent semester’s course schedule, please see Rice Course Schedule.

Restrictions: Enrollment is limited to Graduate level students.

Description: Introduction to fundamental tools in industrial engineering. Topics include productivity analysis, material handling, logistics, design of experiments, quality control, location theory, warehouse design, chain management and scheduling. Instructor Permission Required.

INDE 509 INTRODUCTION TO HUMAN FACTORS ENGINEERING.

Description: Analysis and design of engineering systems considering human characteristics and limitations. Design of control, displays, tools, workstations and groups. Human factors research methods. Instructor Permission Required.

INDE 590 MASTER'S IN INDUSTRIAL ENGINEERING CAPSTONE EXPERIENCE.

Restrictions: Enrollment is limited to Graduate or Visiting Graduate level students.

Industrial engineering is the branch of the engineering profession that is concerned with the design, analysis, and control of production and service systems. Originally, an industrial engineer worked in a manufacturing plant and was involved only with the operating efficiency of workers and machines. Today, industrial engineers are more broadly concerned with productivity and all of the technical problems of production management and control. They may be found in every kind of organization: manufacturing, distribution, transportation, mercantile, and service. Their responsibilities range from the design of unit operations to that of controlling complete production and service systems. Their jobs involve the integration of the physical, financial, economic, computer, and human components of such systems to attain specified goals. Industrial engineering includes activities such as production planning and control; quality control; inventory, equipment, warehouse, and materials management; plant layout; and workstation design.

Operations research is concerned with quantitative decision problems, generally involving the allocation and control of limited resources. Such problems arise, for example, in the operations of industrial firms, financial institutions, health care organizations, transportation systems, and government. The operations research analyst develops and uses mathematical and statistical models to help solve these decision problems. Like engineers, they are problem formulators and solvers. Their work requires the formation of a mathematical model of a system and the analysis and prediction of the consequences of alternate modes of operating the system. The analysis may involve mathematical optimization techniques, probabilistic and statistical methods, experiments, and computer simulations.

The Industrial Engineering and Operations Research (IEOR) offers four graduate programs: a Master of Engineering (MEng), a Master of Science (MS), a Master of Analytics (MAnalytics), and a PhD. This knowledge applies to complex systems in the industrial, service, or public sectors, including energy systems, chains, healthcare systems, and financial systems. Students may concentrate on theoretical studies in preparation for doctoral-level research, or on applications of state-of-the-art techniques to real world problems.

The MEng is a professional, full-time, accelerated professional master degree program. Students learn advanced techniques in IEOR and skills that prepare them to lead teams in developing new engineering solutions: skills in managing complex projects, motivating people, and directing financial and operational matters.

The MS is a full-time technical master degree program. Students focus on both the theory of IEOR techniques and the application of those techniques. The MS is a terminal degree, meaning that students enrolled in the MS program do not typically continue further into the IEOR PhD program. Participants in the program are self-funded the IEOR does not offer funding and students will not be eligible for ASE (academic student employment) appointments funded by the department.

The 11-month in-person Master of Analytics program trains students in data-driven analytical methods and tools for optimization, statistics, simulation, and risk management with relevant industry context so that the graduates are not only highly skilled in the latest tools and fluent with working with large data sets, but also are able to raise the right questions to develop innovative models and find creative solutions to rapidly changing business and industry challenges, and communicate and implement their solutions.

The paramount requirement of a doctoral degree is the successful completion of a thesis on a subject within Industrial Engineering and Operations Research. Research areas may include the investigation of the mathematical foundations of and computational methods for optimization or stochastic models, including risk analysis. Research also may be undertaken to develop methodologies for the design, planning, and or control of systems in a variety of application domains, including chains, energy systems, healthcare systems, and financial systems.

The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:.

If the applicant has completed a basic degree from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 on a 9-point scale (note that individual programs may set higher levels for any of these) and.

The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without the need to enroll in a related or similar graduate program.

Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree.

Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.

Unofficial transcripts must contain specific information including the name of the applicant, name of the school, all courses, grades, units, degree conferral (if applicable).

Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, by the recommender, not the Graduate Admissions.

Evidence of English language proficiency:All applicants who have completed a basic degree from a country or political entity in which the official language is not English are required to submit official evidence of English language proficiency. However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:.

Courses conducted in a language other than English,.

Courses that will be completed after the application is submitted, and.

Official TOEFL score reports must be sent directly from Educational Test Services (ETS). Official IELTS score reports must be sent electronically from the testing center to University of California, Berkeley, Graduate Division, Sproul Hall, Rm 318 MC 5900, Berkeley, CA 94720. TOEFL and IELTS score reports are only valid for two years prior to beginning the graduate program at UC Berkeley. Note: score reports can not expire before the month of June.

Step II: After passing the preliminary or entrance exam, students prepare for their PhD oral qualifying examination. This step lasts one to two years. With the successful passing of the orals, students are advanced to candidacy for the PhD degree.

Step III: Students undertake research for the PhD dissertation under a three-person committee in charge of their research and dissertation. The students then write a dissertation based on the results of this research. On completion of the research, workshops, and approval of the dissertation by the committee, the students are awarded the doctorate.

Every doctoral student is required to take the doctoral entrance examination. Students entering without an MS degree are required to complete all MS degree requirements and may do so by completing the MS course requirements and passing the doctoral entrance exam.

The entrance examination will be offered near the end of every spring semester, approximately one week before finals. Passing the entrance examination is based on both superior performances on all parts of the exam, and on previous coursework. Students are required to take the entire exam at the same time. In order to take the exam, students are expected to perform sufficiently well in their first-year courses.

In particular, students who enter in the fall are expected to take the exam at the end of the spring semester in the same academic year.

Students who have not taken these courses prior to entering the graduate program are required to do so during their first year.

Some students have specific research interests and goals when they enter a doctoral program for others, these interests develop in the process of taking courses and preparing for the entrance examination. In either case, it is imperative that students begin their research as soon as possible after completing their entrance examination.

A minimum of nine graduate courses is required in the major, including those taken prior to the entrance examination. Usually, these are courses taken in this department, but to a very limited extent, courses taken in other departments or at other institutions may be counted as part of this requirement. These courses should provide depth in the student probable research area.

In addition, course work is required in two minor areas. This loose wording reflects the diverse needs of the College. In this department, each minor must consist of six units at the graduate level, at least three of which must be taken for a letter grade. A minor may serve either to strengthen theoretical foundations (e.g., measure-theoretic probability theory), or as an area of application (e.g., transportation). At most one course of one minor can be a course from within this department, as long as this course is distinct from the major. Graduate courses at other institutions may make up part of a minor if the subject matter is appropriate.

In addition to English, the program does not require another language.

Students are expected to take the Qualifying Examination within three semesters after passing the Doctoral Entrance Exam. Priority in department funding (especially NRTs) will be given to students who have passed their Doctoral Entrance Exams and are in their 3rd, 4th, and 5th semesters. Although it is necessary for a student to identify a potential research area and some potential dissertation topics in order to complete this exam, it is not necessary for the student to do a substantial amount of research in the area of the examination.

The student is required to have completed or be currently enrolled in courses that will complete at least one of the two minors at the time of the Qualifying Examination. At least one of the minors completed or being completed at the time of the Examination must consist entirely of courses from outside the department. In addition, at the time of the Qualifying Examination, the student is required to have a specific plan for completing the other minor within two semesters.

At least six weeks prior to the approximate date of the Qualifying Examination, the student needs to begin to arrange for Graduate Division approval of the exam committee. Once the date and the exam committee are decided upon, the student must also request a room in which the exam can be held. Meanwhile, the student should prepare a list of topics, called a syllabus, which will form the basis of the Exam. The syllabus should include topics from the three subject areas to be listed on the Application for Qualifying Examination form, i.e., equivalent to several courses, together with topics from one of the minor areas.

For students who follow these guidelines and the recommendations of the Graduate Adviser and Thesis Adviser, this usually results in quick approval. However, if preparation is judged to be inadequate, they may recommend additional course work and postponement of this Examination.

In many departments, including IEOR, it has been the practice for students to schedule their own Qualifying Examinations. This exam is to be scheduled for three hours, at a time when all Committee members can attend.

If the student performance is judged to be unsatisfactory, the Committee may recommend reexamination, possibly after additional preparation has been completed. If the reasons for the unsatisfactory performance are judged to be major and fundamental, the Committee may recommend that a second attempt be denied.

The IEOR strives to provide every student with an opportunity to gain teaching experience.

It provides the department an opportunity to review the progress of students who have passed the qualifying examination, toward completion of their doctoral dissertation.

During the workshop, the candidate is expected to present a prospective of, and results from, the dissertation research. Dissertation advisers should advise students the appropriate time for the workshops. However, initiation of the workshops is the student responsibility. The student needs to notify the department at least one month in advance of the desired workshop date, and coordinate this date with the dissertation committee. At least two weeks prior to each workshop, the student shall distribute to the dissertation committee a report called the dissertation prospectus. Announcement of the workshop will be made through all the channels used to announce departmental seminars.

Each workshop is divided into two parts. The first part is devoted to a public presentation by the student and subsequent discussion. (Graduate students who have not yet reached this stage in their own program often find that participating in workshops is a valuable educational experience.) The dissertation committee moderates the presentation and discussion, controls the asking of questions by the audience, and calls an end to the first part of the workshop.

At this time, the committee may decide that the candidate progress is unsatisfactory. The committee may require an additional workshop sooner than one year after the unsatisfactory one. Recurrent failure to present a satisfactory prospectus workshop may result in the disqualification of the student and termination of doctoral candidacy.

A completed copy of the thesis must be distributed to the committee at least two weeks before this final workshop. This workshop will follow the same format as other workshops. The committee will inform the candidate any remaining problems or issues with the thesis. If the committee has serious issues with the thesis, it may require an additional defense workshop.

Students are required to complete 24 semester units of upper division and graduate coursework, 12 units of which must be graduate courses in the major taken for a letter grade. IND ENG 298 units do not count towards this requirement.

Beyond these requirements, the program is quite flexible. No than two units of IND ENG 299 may be counted toward the degree. The remainder of the program can include electives outside the department. In addition, they are expected to have completed at least one semester each of upper division courses in probability and in statistics. They should also have competency in a scientific programming language.

Select two from the following (at least one must be a Modeling course):.

Industrial and Commercial Data Systems (fulfills Modeling requirement).

Students may complete the requirements by writing a thesis, rather than taking a comprehensive examination. The course requirements under the thesis option are the same as under the comprehensive option. Under the thesis option, the minimum unit requirement of regular course work is 20 units, not including the thesis.

In addition to course and waiver exam requirements, students are required to complete one of two options: a comprehensive exam or a master project and oral presentation of this project.

Students who are interested in earning a PhD should apply to enter the MS PhD if they do not yet have an MS degree. detailed information on the entrance exam may be found on the Doctoral Degree Requirements tab.

Minimum number of units to complete degree: 25 semester units.

Technical Course work (must be taken for a letter grade):.

Core Courses: All students are required to take IND ENG 240 and IND ENG 241. Students in the FinTech and IPES programs are also required to take IND ENG 242.

FinTech students must select two of the following: IND ENG 221, IND ENG 222, IND ENG 223 IND ENG 224. IP Entrepreneurship Strategy students must take IND ENG 242 and one additional approved INDENG 200+ course, plus two business courses, ENGIN 273 and ENGIN 274.

Leadership Courses (must be taken for a letter grade).

All students must complete 8 semester units of core leadership courses, which must be in the 200-series.

Students must take the capstone integration course each semester.

Special Topics in Industrial Engineering and Operation Research ( (Fundamentals of Machine Learning Data Analytics)).

Minimum number of units to complete degree: 29-semester units.

All students are encouraged to take a Python boot camp (50 hrs in August) in preparation for the technical coursework. The Python boot camp is a 0-unit course offered in August leading up to the program start.

All students must complete four units of the summer internship (individual study) course (10 weeks in May-July) during the final summer semester.

Expand all course descriptions [+]Collapse all course descriptions [-].

Terms offered: Prior to 2007 This introductory course provides students with sufficient background in Python programming language for use in analytics applications as well as potentially conducting research in the area. The course is designed to prepare students for the applied analytics problems and projects they will encounter in advanced analytics courses. It will start with basic programming topics using Python and cover using powerful Python packages such as Numpy, Scipy, Pandas, and Matplotlib that are essential for descriptive, predictive, and prescriptive analytics.

Terms offered: Spring 2017, Spring 2016, Spring 2015 Analysis of the capacity and efficiency of production systems. Development of analytical tools for improving efficiency, customer service, and profitability of production environments. Design and development of effective industrial production planning systems. Modelling principles are illustrated by reviewing actual large-scale planning systems successfully implemented for naval ship overhaul and for semiconductor manufacturing.Economics and Dynamics of Production: [+].

Prerequisites: 262A (may be taken concurrently), Mathematics 104 recommended.

Terms offered: Spring 2023, Fall 2022, Spring 2022 A course on financial concepts useful for engineers that will cover, among other topics, those of interest rates, present values, arbitrage, geometric Brownian motion, options pricing, portfolio optimization. The Black-Scholes option-pricing formula will be derived and studied. Stochastic simulation ideas will be introduced and used to obtain the risk-neutral geometric Brownian motion values for certain types of Asian, barrier, and lookback options. Portfolio optimization problems will be considered both from a mean-variance and from a utility function point of view. Methods for evaluating real options will be presented. The use of mathematical optimization models as a framework for analyzing financial engineering problems will be shown.Introduction to Financial Engineering: [+].

Terms offered: Spring 2023, Fall 2022, Spring 2022 Introductory graduate level course, focusing on applications of operations research techniques, e.g., probability, statistics, and optimization, to financial engineering. The course starts with a quick review of 221, including no-arbitrage theory, complete market, risk-neutral pricing, and hedging in discrete model, as well as basic probability and statistical tools. It then covers Brownian motion, martingales, and Ito calculus, and deals with risk-neutral pricing in continuous time models. Standard topics include Girsanov transformation, martingale representation theorem, Feyman-Kac formula, and American and exotic option pricings. Simulation techniques will be discussed at the end of the semester, and MATLAB (or C or S-Plus) will be used for computation.Financial Engineering Systems I: [+].

Prerequisites: 221 or equivalent 172 or Statistics 134 or a one-semester probability course.

Terms offered: Fall 2022, Fall 2021, Fall 2020 Advanced graduate course for Ph.D. students interested in pursuing a professional research career in financial engineering. The course will start with a quick review of 222: the basics of Brownian motion, martingales, Ito calculus, risk-neutral pricing in continuous time models. It then covers rigorously and in depth the most fundamental probability concepts for financial engineers, including stochastic integral, stochastic differential equations, and semi-martingales. The second half of the course will discuss the most recent topics in financial engineering, such as credit risk and analysis, risk measures and portfolio optimization, and liquidity risk and models.Financial Engineering Systems II: [+].

Terms offered: Spring 2019, Spring 2018 The course aims to train students in hands-on statistical, optimization, and data analytics for quantitative portfolio and risk management. In addition, the course will introduce elements of financial markets and asset classes. The emphasis will be on computational methods such as variants of GARCH, Black-Litterman, conic optimization, Monte Carlo simulation for risk and optimization, factor modeling. Students will undertake computational assignments and a group project. They will also manage hypothetical portfolios throughout the course.Portfolio and Risk Analytics: [+].

Terms offered: Prior to 2007 The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experience.Introduction to Convex Optimization: [+].

Formerly known as: Electrical Engineering C227A Industrial Engin and Oper Research C227A.

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2019, Spring 2018, Spring 2017 Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics, control, finance, data mining, operations research.Convex Optimization and Approximation: [+].

Terms offered: Spring 2023 This course is geared towards understanding operational, strategic, and tactical aspects of chain man agement. Topics covered are from a broad range that includes demand modeling, inventory management, facility location as well as process flexibility, contracting, and auctions. Important models

Score must be sent to Georgia Institute of Technology, Graduate .

Determine if you will pay in-state or out-of-state tuition. For example, if you are an in-state resident and planning to take six credits for the Master of Architecture degree, the tuition cost will be $4,518.

The MS in Industrial & Systems Engineering (MSISE) program is designed for engineers and related technical professionals aspiring to achieve the highest levels of responsibility and leadership in the workplace. As an MSISE student, you will be broadly educated in all aspects of technical enterprises. The MSISE program is excellent preparation for industrial engineering program graduates who want to acquire substantial depth with respect to industrial engineering methods and the theory of the firm. This degree is also relevant if you are a graduate from another technical area and intend to leverage your existing skills toward the pursuit of responsibility for the profitability and growth of your organization.

The dual MBA MSE in Industrial and Systems Engineering has been developed to meet the need for professionals who have expertise in both engineering and management.

It is open to students who have completed a bachelor of science degree in engineering, a physical science, computer science, or applied mathematics.

The program is offered jointly by the College of Business and the College of Engineering and Computer Science. It allows students to receive both the MBA and MSE-ISE simultaneously upon completion of the required 57-66 credit hours.

University of Michigan-Dearborn students who have been admitted to the program may take up to 6 graduate business credits during the final semester of their program.

Completion of a bachelor of science degree in engineering, a physical science, computer science, or applied mathematics.

Tailor the degree to your own interests with an optional MBA concentration in Accounting, Business Analytics, Finance, International Business, Information Systems Management, Marketing, or Chain Management. (Students electing the Information Systems concentration in the MSE-ISE may not earn the MBA concentration in Information Systems Management. Students electing the Operations Research and Management Science concentration in the MSE-ISE may not earn the MBA concentration in Chain Management.).

Alternatively, you may elect courses from our extensive list of graduate business courses. Students may count up to 3 credits in non-business graduate courses or Business Internship (BI 500, 506, or 560) toward the MBA electives with the approval of the Graduate Programs Office.

Four courses may be taken from one concentration area or from any combination of the following four areas. It is understood that this dual degree program will incorporate any future additions to, or modifications of, the current MSE-ISE concentrations:.

IMSE 519 Quantitative Methods in Quality Engineering (3).

Complete no than 4 AIM, MBA Concentration, and MBA Elective courses (12 credits) in any one discipline. (This does not to courses associated with the MSE-ISE portion of the dual-degree program.).

Complete graduate business courses in at least 5 different disciplines.

No single course may be counted toward than one requirement or concentration in the dual-degree program. (For example, you may not use the same course to fulfill both an AIM and a concentration requirement).

Students may waive ACC 505, BE 530, BPS 516, FIN 531, ISM MIS 525, MKT 515, and OB 510 if they have equivalent courses in an AACSB business program completed within the previous 10 years and have earned at least a 3.2 post-60 GPA (that is, your GPA in courses taken after your first 60 credit hours). Students who do not meet these criteria may request to have their courses evaluated for waiver credit at the time of admission. Students must have earned a B or better in equivalent courses as a part of a degree program completed within the previous 10 years.

Regardless of waiver and exemption credits granted, students must earn at least 57 credits in the dual-degree program.

In addition, up to 6 transfer credits for previous equivalent graduate coursework can be applied to the degree if those credits have not been counted toward a degree.

See also the MSE-Industrial and Systems Engineering program goals.

Industrial engineers find the most effective way to turn the basic factors of production — people, materials, machines, time, energy, and money — into almost every product and service we consume. The best engineers are adept not only at managing employees and technology but also at optimizing the organization of a business.

That’s why, at the School of Engineering, we ask our students to take the unique approach of examining relationships rather than components. The MS in Industrial Engineering program teaches you to search for similarities in concepts, laws, and models across disciplines. From there, it’s up to you to adapt, integrate, and exploit these similarities in innovative ways.

Because industrial engineers often work on multidisciplinary teams, we offer an elective-heavy curriculum. That approach lets you build expertise in whichever subjects best fit your career interests. These specialties include:.

You must complete a total of 30 credits to obtain this degree.

Topics in this course include facilities design for global competitiveness, strategic master-site planning, site selection, factory layout and design, facility-management systems and materials handling and storage planning. Also presented are guidance on selecting alternative facility plans and application of queuing methods and computer modeling for facility design and evaluation.

This course examines modeling and simulation of complex industrial, commercial and service systems, such as factories and hospitals. Students develop, run and test several simulation models using different software packages. Prerequisite: Computer literacy.

You must take 3 electives from manufacturing or industrial engineering for a total of 9 credits, referred to as the IE MN elective block. These courses include any 3 selections from the following list:.

Students in this course gain an understanding of how companies plan, source, make and deliver their products with a global competitive advantage. The course stresses the engineering components in developing an integrated chain that covers the entire manufacturing enterprise. It looks at the -chain infrastructure and the velocities of different models. The focus is on understanding and detecting the constraints of the infrastructure and the lowest common denominator of the information system used. Students also gain an understanding of logistical networks and the optimizing of the various traffic and location alternatives. Synchronization of and demand is examined in detail, looking at variability in both processes with the objective of maximizing throughput and capacity, emphasizing partnering, e-commerce and the bullwhip effect. Finally, the course establishes global performance measurements that compare companies in different industries.

This is a survey course in basic and advanced manufacturing planning and control systems, covering short-term forecasting systems, master production scheduling, material requirements planning, inventory management, capacity management, production activity control and just-in-time.

This course introduces major concepts and methods associated with Management Science, which deals with the application of quantitative modeling and analysis to management problems. Students learn to employ important analytical tools, to determine the assumptions used, and to recognize the limitations of such methods. The course discusses methods of linear and n ar programming, queuing, decision analysis, simulations and game theory. The course also introduces modeling with spreadsheets. Prerequisite: Graduate Standing.

This course focuses on developing a deeper understanding of the role that operations management plays in determining business strategy and in developing competitive advantage. The primary emphasis is on develop and effectively manage operations in knowledge-intensive enterprises. Participants discuss the operational design and managerial implications when the emphasis of the operations group is on knowledge management than on production and facilities management managing the effective integration of technology, people and operating systems understanding the complexities and challenges of operations management the challenges of developing and managing chain networks and the critical role of technology in developing operational capabilities in an organization. Prerequisite: Graduate Standing.

This course focuses on managing technology based projects, ranging from individual research and development to large-scale and complex technological systems. It covers topics such as feasibility and risk analyses, project selection and portfolio optimization, functional and administrative structures, coordination and scheduling of activities, personnel planning, negotiations and contracts, cost estimation, capital budgeting, cost controls and effective matrix management. Prerequisite: Adviser’s approval and Graduate Standing.

This course examines and applies the valuation and management of intangible assets in designing and managing post-industrial organizations. As organizations increasingly rely on technology to produce value, these technological solutions require interactions with other forms of value creation like Human Capital Management, Intellectual Property development and Organization Culture. The first part of the course focuses on human capital engineering using an interdisciplinary approach, drawing on diverse fields including industrial-organizational psychology, industrial engineering, economics and artificial intelligence to create a holistic view of how work in its various forms creates value. The second part of the course addresses workforce analytics, providing the student with a knowledge and understanding of current best practices, issues, and decision points in building an effective human capital analytic program. This part of the course will also focus on data structure and design to enable automation and predictive modeling and will place an emphasis on technology-enabled reporting.

The dynamics of technology and the pressures of competition drive enterprises to make their product development and production processes strategically effective and economically efficient in time and cost. The course deals with the state of the art in new product activities for services and manufacturing firms and examines in-depth the marketing, technology and manufacturing technology linkages. Prerequisite: Graduate Standing.

The course focuses on effectively managing technological change and innovation, which is accomplished with a dual perspective. One perspective is based on individual, group and organizational theory, research and practice. This body of literature, viewpoints and experience provide essential guides to manage successfully the introduction of newtechnologies. Realizing the full potential of new technologies requires effectively managing change to assure the commitment of all stakeholders. The second perspective is based on innovation theory, research and practice. This body of literature, viewpoints and experience provide key insights to for effectively managing the process of innovation and the impact of innovation on all parts of an enterprise. Specifically, the course explores a firm’s explicit need to manage and inspire people so they can communicate and innovate effectively. Prerequisite: Adviser’s approval and graduate standing.

Students analyze and discuss current topics in various fields. Prerequisites: Graduate standing and Department’s Chair’s permission.

An additional 3 free electives are taken from anywhere in the Industrial Engineering or Management of Technology programs. In addition, these remaining 3 free electives may be taken from any other graduate curriculum with the approval of the Program Director to ensure their compatibility with your professional objectives for the remaining 9 credits that are needed to fulfill the degree requirements.

You should elect other courses in consultation with your adviser. Concentrations in areas suited to your career interests are encouraged (e.g., manufacturing, mechanical engineering, operations management, construction management, transportation engineering or management, and management of technology). Courses from computer science or management may supplement such a concentration.

Operations research is a mathematical science concerned with optimal decision making and the modeling of deterministic and probabilistic systems. Its focus and field of application are interdisciplinary, embracing a broad range of quantitative techniques. Industrial engineering is concerned with the design, improvement, and installation of integrated systems of personnel, material, and equipment. Together, operations research and industrial engineering provide a rational approach to engineering and managerial problem solving through deliberate application of scientific methods.

In practice, operations research and industrial engineering address both the performance objectives and the resource constraints of an organization, working toward the establishment of policies that are most beneficial to the organization as a whole. The function of the operations research analyst or the industrial engineer is to guide decision making by identifying underlying cause-and-effect relationships, developing and proposing courses of action, establishing criteria by which to judge their effectiveness, and evaluating their probable effects.

The program in operations research and industrial engineering is designed to allow students to develop the technical, analytic, and managerial skills necessary to perform these tasks successfully. The graduate program in Operations Research Industrial Engineering offers M.S. and Ph.D. degrees.

Industrial engineers design and improve systems that use people, machines, information, materials, and energy to make and deliver products or provide services. They improve system performance by solving problems whose outcomes are influenced by complicated and uncertain interactions. If you are thinking a career as an industrial engineer, the Stewart School of Industrial and Systems Engineering (ISyE) is a natural place to begin your career path. Georgia Tech program features a track option that allow students to either obtain a broad industrial engineering education or specialize in economic and financial systems operations research quality and statistics chain engineering.

Stewart School of Industrial and Systems Engineering Website.

Shown in a Delta maintenance facility are Todd Herrington, general manager of fleet projects at the company, and Chuck Zhang, a professor in Georgia Tech Stewart School of Industrial and Systems Engineering.

Ben Wang, executive director of the Georgia Tech Manufacturing Institute (GTMI) and the Eugene C. Gwaltney Jr. Chair in Manufacturing Systems for the H. Milton Stewart School of Industrial and Systems Engineering.