Undergraduate Courses

Credit Information: (1+1+0) 1 P/F
Description:
Introduction and orientation to IE: concepts and approaches; illustrations of main methods and applications presented by a series of lectures given by IE faculty; overview of departmental laboratories, basic information technologies, and software including mathematical packages and web-based applications.
Credit Information: (2+0+2) 3
Description:
Introduction to object-oriented analysis and design: data encapsulation, inheritance, polymorphism, software engineering methodologies, UML; introduction to C++ programming language: class, inheritance, polymophism, basic input/output, operator overloading, exception handling, templates; fundamental data structures: array, list, tree, binary tree; fundamental algorithms: searching, sorting, recursion.
Prerequisite: CmpE 150 or equivalent.
Credit Information: (3+2+0) 4
Description:
Modelling concepts; linear programming models; simplex and dual simplex methods; duality and sensitivity analysis; transportation, transshipment and assignment problems; integer programming; branch and bound techniques; cutting plane algorithm; basic problems in network theory: minimum spanning tree, shortest path, maximum flow
Prerequisite: Math 201.
Credit Information: (3+1+1) 4
Description:
Materials and properties; structure and manufacturing properties of metals; metal casting; bulk deformation processes (rolling, extrusion, forging); sheet-metal forming; machining processes (turning, drilling and milling); abrasive machining; processing of plastics
Prerequisite: PHYS 121, CHEM 105.
Credit Information: (3+0+0) 3
Description:
Basic topics in probability theory; sample space, probability, and conditional probability; random variables, marginal, joint and conditional distributions; expectations and conditional expectations; hypergeometric, binomial, geometric distributions and their implications in IE/OR; Poisson, exponential, Erlang, gamma distributions and the Poisson arrival model; moment generating functions and Laplace transforms; law of large numbers, central limit theorem, and the Normal distribution; numerical and computational aspects of random variable generation.
Credit Information: (3+0+0) 3
Description:
Basic topics in parametric statistics; estimation, confidence intervals, and hypothesis testing; analysis of variance, regression and correlation analysis; goodness of fit tests; application in statistical quality control, demand forecasting, and other IE/OR topics; elementary design of experiments and data collection; computer implementations using available up-to-date statistical software.
Prerequisite: IE 255 or equivalent.
Credit Information: (3+0+2) 4
Description:
Nonlinear programming; optimization in one variable, convexity, unconstrained and constrained optimization in many variables, Kuhn-Tucker optimality conditions, direct search and gradient methods; computational complexity; major heuristic approaches: simulated annealing, neural networks, tabu search, genetic algorithms.
Prerequisite: IE 202 or equivalent.
Credit Information: (3+2+0) 4
Description:
Nonlinear programming; optimization in one variable, convexity, unconstrained and constrained optimization in many variables, Karush-Kuhn-Tucker optimality conditions, direct search and gradient methods; deterministic and probabilistic dynamic programming; Markov chains; Markovian decision process; Poisson process and queueing models.
Prerequisite: IE 202, IE 255
Credit Information: (3+0+2) 4
Description:
Basic concepts of discrete-event simulation modeling/analysis. Event-scheduling versus Process-interaction approach. Random number and random variate generation; inverse transformation and other selected techniques. Input data analysis and goodness of fit tests. Specific computer simulation languages. Analysis of simulation output and model validation.
Prerequisite: IE 256.
Credit Information: (3+0+2) 4
Description:
Economics and engineering decisions; principles of decision theory; generation and evaluation of alternatives; unconstrained and constrained optimization; duality and sensitivity analysis; application of LP; network models; simulation; case studies.
Prerequisite: MATH 201 or equivalent.
Credit Information: (3+0+2) 4
Description:
Nature and classification of production systems. Product design. Forecasting methods; simple linear regression, moving average and exponential smoothing methods. Capacity requirements planning. Design of discrete production systems: product–based layout and assembly line balancing; process-based layout and design of work stations; group technology and cell design; material handling and storage systems. Facility location; discrete and continuous space location models.
Prerequisite: IE 256 and IE 202.
Credit Information: (3+0+2) 4
Description:
Economic analysis for engineering decision making; the finance function in an industrial enterprise, time value of money; basic interest formulas; annual cost comparison; present value analysis; rate of return; depreciation and taxes; multiple alternatives; mathematical models for equipment replacement; introduction to decision analysis; concepts of cost engineering.
Credit Information: (3+1+0) 3
Description:
Introduction to systems science and systems engineering; fundamental concepts, philosophy and historical development of systems science. Analogical dynamic systems in electrical, hydraulic and mechanical fields; demonstration of general systems analogy on industrial, socio-economic and managerial models. Analysis tools for linear dynamic systems; non-linear model structures and inherent difficulties in their mathematical solution; equilibrium and stability analysis. Introduction to stock-flow modeling and formulation principles to represent socio-technical problems. Simulation method and software for large scale, time-delayed, non-linear dynamic models.
Prerequisite: MATH 202
Credit Information: (3+0+2) 4
Description:
Fundamentals of supply chain management and enterprise resources planning (ERP); aggregate production planning: static, dynamic, nonlinear and lot sizing models; operations scheduling: flow shops and job shops; materials management and materials requirement planning (MRP); capacity resources planning (CRP); distribution system management; implementation of manufacturing management strategies.
Prerequisite: IE 312 or equivalent.
Credit Information: (2+0+2) 3
Description:
Fundamentals of CIM and automation; CAD/CAM, numerical control manufacturing systems; Robotics, Flexible manufacturing systems; lab assignments on automation technologies such as CAD/CAM, robotics, FMS design and simulation etc.
Prerequisite: IE 306 or equivalent.
Credit Information: (2+0+2) 3
Description:
Overview of optimization, simulation, stochastic and multiobjective models; various application areas, underlying assumptions, and critical technical considerations of optimization models; typical implementation problems, practical points and obstacles encountered in applying operations research models to real life problems.
Prerequisite: IE 202 or IE 310.
Credit Information: (2+0+2) 3
Description:
Principles of quality control systems; process control concepts; specification and tolerances; process capability studies; control charts; acceptance sampling plans; cost aspects of quality decisions; quality improvement programs; quality information systems.
Prerequisite: IE 256 or equivalent.
Credit Information: (2+0+2) 3
Description:
Portraying and comparing distributions, quantile plots, box plots, stem and leaf diagrams; transformations; smoothing; multivariate data, scatter plot matrices, Kleiner-Hartigan trees, Chernoff faces, star diagrams, glyphs; developing regression models, outlier detection, partial residual plots, Cp plots; ridge regression; robust regression.
Prerequisite: IE 256 or consent of the instructor.
Credit Information: (3+0+1) 3
Description:
Principles of ergonomics and human factors; their applications to the design and management of industrial systems. Human performance; human-technology interaction; job, work environment and product design; occupational safety and health. Methods development, work measurement and work standards. Productivity and quality improvement; cost reduction. Industrial applications.
Credit Information:
Description:
Nonlinear programming; methods for one dimensional optimization, convex sets and functions, unconstrained optimization, constrained optimization, first and second order optimality conditions, derivative free methods, basic descent algorithms, convex optimization. Machine learning; perceptrons, self-organizing maps, classification, clustering, support vector machines for classification and regression.
Prerequisite: IE 305
Credit Information: (3+0+1) 3
Description:
Analysis of the planning process. Multiple objective and goal programming; identification and discussion of the efficient frontier. Data Envelopment Analysis. Project Management; critical path based methods; random activity times; mathematical programming formulation; renewable and non-renewable resource constraints; risk analysis. Case studies.
Prerequisite: IE 202.
Credit Information: (2+0+2) 3
Description:
Description of the organization and management, market study, choice of production capacity, project engineering, cost and revenue estimation, financing and preparation of financial tableaus, economic analysis, case studies.
Prerequisite: IE 341 or consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Human capital concept; people, productivity and the quality of working life; evaluation of human resources management; legal and social contexts of personnel decisions; analyzing and designing jobs; determining human resources requirement; recruiting; screening and selecting employees; orienting and training employees; identifying and developing management talent; appraising employee performance; managing careers; compensation management; assessing the costs and benefits of personnel activities; international dimensions of human resources management.
Credit Information: (3+0+0) 3
Description:
Special topics in Industrial Engineering selected to suit research interests of the faculty.
Credit Information:
Description:
This course is designed to provide students with the tools and knowledge necessary to conduct a simulation supported analysis of socio-technical problems using agent-based models (ABMs). Students will gain understanding and awareness of the fundamental differences of agent-based modeling from other simulation modeling approaches, and nature of problems/objectives that ABMs fit the best. Besides, students will develop competency in building ABMs, analyzing and interpreting results from these models, and communicating a complete simulation supported analysis cycle to peers/clients. Example models used during the semester will be drawn from social, economic, environmental, industrial, energy and logistic/transportation problems. For the term project, students will go through a model supported analysis process as they develop an ABM in order to analyze a problem from their own areas of interest.
Credit Information: (3+0+0) 3
Description:
Special topics in Industrial Engineering selected to suit the interests of the individual students. The course is designed to give student an opportunity to do independent work at an advanced level.
Credit Information: ((1-3)+0+0) 3
Description:
Design or research projects will be the main topics. Students with special interest and qualifications may he permitted to take this course.
Credit Information: (0+0+8) 4
Description:
Listed under Engineering Core Courses.