|Course #||Units (sem)||Course Name||Prerequisites||Time Offered|
|IESM 300||3||Probability Theory||None||Fall 1|
|IESM 301||3||Analysis and Design of Data Systems||None||Fall 1|
|IESM 320||3||Operations Research 1||None||Fall 1|
|IESM 310||3||Engineering Statistics||300||Spring 1|
|IESM 340||3||Engineering Economics||None||Spring 1|
|IESM 321||3||Operations Research 2||300, 320||Spring 1|
|IESM 331||3||Production System Analysis||310, 321||Fall 2|
|IESM 311||3||Quality Assurance and Management||310||Fall 2|
|IESM 330||3||Simulation of Industrial Engineering Systems||310||Spring 2|
|IESM 395||2||Capstone Preparation||Second-year standing||Fall 2|
|IESM 396||4||Capstone: Thesis||395||Spring 2|
|IESM 397||1||Capstone: Project||395||Spring 2|
|IESM 050||–||Intro to MATLAB||None||Fall 2|
|IESM 315||3||Design and Analysis of Experiments||310||Fall 2|
|IESM 390||3||Integrative Project in Modern Production Methods||Second-year standing||Spring 2|
|IESM 339||3||Production and Operations Management||321||–|
|IESM 399||3||Special Topics in IESM||Consent of instructor||–|
|IESM 391||3||Individual Study||Approval of the program chair and the instructor||–|
|IESM 313||3||Data Mining and Predictive Analytics||None||–|
|IESM 347||3||Design and Innovation of Information Services||None||–|
|Course #||Units (sem)||Course Name||Prerequisites||Time Offered|
|IESM 345||3||Supply chain Management||None||–|
|IESM 348||3||Investment Management||None||–|
|IESM 349||3||Enabling Competitive Advantage through Information Technology||None||–|
|IESM 350||3||Alternative Energy: Technology, Environment, and Economics||None (note: also fulfills the university-wide Environmental Sciences requirement.)||–|
|IESM 351||3||Engineering Green Buildings: Waste, Water, and Energy||None (note: also fulfills the university-wide Environmental Sciences requirement.)||–|
|IESM 352||3||Decision-Making Tools for Energy Use and Generation||None||–|
DESIGN & MANUFACTURING
|IESM 360||3||Computer-aided Design||None||Fall 1|
|IESM 361||3||Computer-aided Manufacturing||None||Spring 1|
|IESM 362||3||Advanced CAD/CAM Applications||360 or 361||Spring 2|
Prerequisite: None. Three hours of lecture per week. This course is an introduction to the mathematical study of randomness and uncertainty. Axioms of probability; conditional probability and independence; combinatorial analysis and application; discrete and continuous random variables; expectation, variance and covariance; transformation of random variables; moment generating functions; characteristic functions; limit theorems; selected probability models; binomial; polynomial; Poisson; hypergeometric; normal; uniform; exponential; lognormal and gamma distributions; simulations; bivariate normal vector; the simplest time‐dependent stochastic processes; Markov chains; Poisson process; the Brownian motion; the Black‐Scholes option pricing formula; engineering applications.
Prerequisite: None. Three hours of lecture per week. Review of data systems and data processing functions; technology; organization and management; emphasizing industrial and commercial application requirements and economic performance criteria; survey of systems analysis, design; modeling and implementation; tools and techniques; design-oriented term project.
Prerequisite: None. Deterministic linear optimization models and applications: linear programming, duality, postoptimality (sensitivity and parametric) analysis; formulation of linear programs; optimal allocation and control problems in industry and environmental studies; convex sets; properties of optimal solutions; simplex and revised simplex algorithms; problems with special structures, e.g., transportation and assignment problems, network problems.
Prerequisite: 300. Three hours of lecture per week. Elements of statistical inference; point and interval estimation; regression and correlation; hypothesis testing; maximum likelihood estimation; Bayesian updating; use of statistical software.
Prerequisite: None. Three hours of lecture per week. Analysis of economic investment alternatives; concepts of the time value of money and minimum attractive rate of return; cash flow analysis using various accepted criteria, e.g., present worth, future worth, internal rate of return, external rate of return; depreciation and taxes; decision making under uncertainty; benefit-cost analysis; effects of inflation (relative price changes).
Prerequisite: 300, 320. Three hours of lecture per week. Deterministic and stochastic models and methods in Operations Research; network analysis; integer programming; unconstrained and constrained optimization; deterministic and stochastic dynamic programming; Markov chains; queuing theory.
Prerequisite: 310 and 321 (both can be taken concurrently). Three hours of lecture per week. Analysis, design and management of production systems. Topics covered include productivity measurement; forecasting techniques; project planning; line balancing; inventory systems; aggregate planning; master scheduling; operations scheduling; facilities location; and modern approaches to production management such as Just-In-Time production.
Prerequisite: 310. Three hours of lecture per week. Principles and methods of statistical process control; quality engineering; total quality management, as applied to manufacturing and service industries.
Prerequisite: 310.Three hours of lecture per week. Design, programming and statistical analysis issues in simulation study of industrial and operational systems; generation of random variables with specified distributions; variance reduction techniques; statistical analysis of output data; case studies; term project.
Prerequisite: second-year standing. Review of Capstone objectives and procedure; faculty and industry representatives’ presentation of suggested research topics; field trips to the local companies; literature survey and classroom presentation by students. Students select the topic of their capstone project and the supervisor and prepare and submit the project proposal. Students draft a literature survey on their selected topic, which will constitute a section or chapter of the capstone project report. The completed and approved Proposal for Culminating Experience Requirement form must be filed in the College office prior to the end of the course.
Prerequisite: 395. One of the two Capstone options offered by the Program. Supervised individual study employing concepts and methods learned in the program to solve a problem of significant importance from a practical or theoretical standpoint. This option is more appropriate for those students who are interested in an in-depth R&D experience.
Prerequisite: 395. One of the two Capstone options offered by the Program. Supervised individual study employing concepts and methods learned in the program to solve a problem from a practical standpoint. This option is more appropriate for those students who are inclined to practical work and do not necessarily aspire for intensive research training.
Prerequisite: none. Three hours of lecture per week. MATLAB (MATrix LABoratory) is a leading software used for numerical analysis. It provides an environment for computation and visualization. Students will work toward developing a working knowledge of MATLAB to implement and test algorithms, thus enabling a deeper understanding of and facility working with analytical engineering tools.
Prerequisite: 310. Three hours of lecture per week. Principles and methods of design and analysis of experiments in engineering and other fields; real-world applications of experimental design; completely randomized designs; randomized blocks; latin squares, analysis of variance (ANOVA); factorial and fractional factorial designs; regression modeling and nonparametric methods in analysis of variance.
Prerequisite: second-year standing. Two hours of lecture and discussion and six hours of field work per week. This is a project-based course that involves field work (in manufacturing or service organizations) and integrates and synthesizes knowledge gained from several courses (e.g., operations management, operations research, statistics, and quality management). Student teams, supported by several faculty members, will work with industrial companies to identify improvement opportunities and help in implementing them.
Prerequisite: 321. This course will introduce concepts and techniques for design, planning and control of manufacturing and service operations. It was created in collaboration with the MIT Sloan of Management course, Operations Management. The course provides basic definitions of operations management terms; tools and techniques for analyzing operations; and strategic context for making operational decisions. It incorporates HBS cases and HBR articles.
Prerequisite: consent of instructor. Advanced studies on special topics selected on annual basis.
Prerequisite: approval of the program chair and the instructor. Special study of a particular problem under the direction of a faculty member. The student must present a written, detailed report of the work accomplished.
Prerequisite: None. Exploratory Data Analysis; Classification: Decision Trees, Model Evaluation, Overfitting; Linear and Logistic Regression; Association Analysis; Cluster Analysis; Anomaly Detection; Model Building and Validation.
Prerequisite: None. The course aims to provide with theoretical and practical insight into the key concepts and issues that guide the design and development of modern information services. The students will explore the contextual considerations of designing information services through in-depth examination of expanding possibilities for innovation and associated risks that modern-day devices, data, content, systems and infrastructures offer. Of particular interest will be the structuring and design of problems in industries with complex ecosystems using Soft Systems Methodology and Unified Modeling Language with special stress on capturing and analyzing information requirements of parties involved.
As part of the course, students will design their own information service to address a problem of their choice, using all the depth of technical and social issues facing companies, individual users and societies.
Prerequisite: none. Seminar exploring the complexities of creating and sustaining an entrepreneurial venture. We concentrate on the impact of innovative behavior and its implication to decision making. The primary focus of the course is on the behaviors involved in forming new enterprises: recognizing and evaluating opportunities; developing a network of support; building an organization; acquiring resources; identifying customers; estimating demand; selling, writing and presenting a business plan; and exploring the ethical issues entrepreneurs face. The course consists of case studies and discussion, in-class exercises, readings, guest speakers, and an outside project.
Prerequisite: none. The course introduces investment management with the focus on practical applications and analysis of the theories covered. It provides students training on understanding the investment process and the intuition behind it. During the course, students will get basic knowledge of financial markets, valuation of investment tools, investment strategies, and management tools. Either you want to pursue a career as investment professional, as a manager of a company or as an individual investor, you will need to make decisions requiring knowledge and good understanding of financial markets and investments. In this course you will focus on applications and methods, using real-world data, cases and project to make strategic inferences and fully understand the topics covered.
Prerequisite: none. The course reviews: the basics of the alternative energy generation options; the respective technologies and resources; as well as the economic, environmental and urban aspects of their introduction into the modern society. Topics include: the role and the current status of the alternative energy in the modern society; energy and force – phenomena and units; solar radiation characteristics; carbon cycle and traditional sources of energy; solar thermal processes (options), such as wind, solar heat, ocean heat and wave, solar hot water, solar electricity, passive solar; solar photon processes, such as solar photovoltaics – from principles to systems, biomass, biofuel, biogas, etc; nuclear power – fusion and fission; infrastructure related economics; distributed power; energy storage, etc.
Prerequisite: none. The course introduces students to the latest practices and technologies in reducing the environmental impact of buildings and the built environment with specific focus on energy, water, and waste. Students will be expected to gain analytical and quantitative skills in analyzing energy, water, and waste with the aim of estimating ways to achieve “carbon neutrality,” “zero emissions” among other green goals. Students will also be introduced to green building norms established by the US Green Building Council as well as other international comparatives. Prerequisite: None.
Prerequisite: none. The course reviews: The course will focus on non‐design decision tools. The analytical tools to be covered will include financial (payback period, NPV, and IRR), economic (Input‐Output, Cost‐Benefit), and environmental (Life Cycle Assessment, McKinsey Carbon Abatement Analysis, Carbon Footprint, Water Footprint, Ecological Footprint). Many of these analyses will be relevant for a wide range of industries including transportation, construction, manufacturing, as well as energy. The course will use cases and simulations to teach and deepen understanding of core concepts and methodologies.
DESIGN & MANUFACTURING
Prerequisite: none. Fundamentals of part design; computer-aided design tools and data structures; geometric modeling; transformations; CAD/CAM data exchange; mechanical assembly.
Prerequisite: none. Introduction to manufacturing processes; cutting fundamentals; design for manufacturability; design for machining; process engineering; NC fundamentals; manual NC programming; computer-aided part programming; group technology.
Prerequisite: 360 or 361. Advanced surface and solid modeling; top down and bottom up assembly; finite element analysis; sensitivity studies; optimization; advanced computer-aided part programming and manufacturing; mold design; team work.