2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering Compulsory Courses IENG540 Optimization Models and Algorithms In the course important deterministic optimization models and methods will be discussed including continuous Linear Optimization (LO) model and classical Simplex method. Next, continuous unconstrained and constrained Nonlinear Optimization (NLO) models will be considered. Time permitting; several NLO models with special structure such as Quadratic, and Geometric (Posynomial) Optimization problems will be discussed. In addition to classical methods for NLO, a class of Interior-Point Methods (IPM) for LO and some classes of NLO will be introduced. Furthermore, Linear Integer Optimization models and techniques will be considered. IENG560 Probabilistic Analysis The course aims to provide a working knowledge of probability theory and acquaint the students with a few fundamental stochastic processes. The emphasis is on understanding and applying the machinery of probability. The course also provides an introduction to stochastic processes (namely Poisson and Markov processes) whose applications abound in a wide range of areas. This constitutes the minimum that any IE/OR graduate student should know on stochastic processes. IENG590 Seminar These seminars introduce the student to the various research topics in industrial engineering and operations research. Speakers from the department, as well as from the academia and industry, present their areas of research interests and activities. The seminars typically include an extensive question-and-answer type discussion phase. Such discussions are instrumental in guiding graduate students towards individual research topics for their thesis work. Elective Methodology Courses IENG532 System Modeling and Simulation IENG 532 is an advanced course in simulation. Although the student will be re-familiarized with the fundamentals of modeling and programming, the contribution of this course is in the more advanced concepts. These include the theory of random number generation, random variate generation, statistical post-simulation analysis, variance reduction, and under-thehood programming aspects of simulation software. That is, rather than demonstrating what simulation is and how to use simulation, the course dwells more on how and why simulation works. IENG555 Heuristic Optimization Most of the problems in practice may be non-separable, non-linear, non-convex. Heuristic optimization provides reasonable solutions for such systems. This course aims to develop an advanced level of knowledge on heuristic optimization. The techniques covered in this course include greedy heuristics, genetic algorithm, tabu search. Simulated annealing,
differential evolution, ant colony optimization and hybrid heuristics as well as some multiobjective optimization procedures. IENG561 Applied Stochastic Processes l The course begins with an in-depth coverage of conditional probabilities and conditional expectations. Then Markov chains, Poisson processes, continuous-time Markov processes and renewal processes are studied. IENG580 Statistical Analysis The course will cover unbiased, consistent, efficient, asymptotically normal estimators, Cramer-Rao inequality, Sufficient statistics, Confidence intervals, Method of least squares, linear and multiple regression, Testing hypotheses, Goodness of fit tests, hypothesis of independence, Neyman-Pearson theorem and selected topics from factor analysis, experimental design, classification theory and cluster analysis. IENG581 Experimental Design Using statistical design of experiments tools both design and statistical analysis issues are discussed. Data collection, analysis and interpretation methods will be discussed. The course first introduces the basic definitions and terminology of experimental design methodology. After reviewing ANOVA and major statistical hypothesis tests, choice of sample size in designed experiments and randomization and blocking and confounding will be discussed. Most of the course will be based on full and fractional factorial designs. Finally response surface methods and designs will be introduced. Computer software packages (Design-Expert, Minitab) to implement the methods presented will be illustrated extensively, and students are expected to learn to use these packages. IENG600 Mathematics of Operations Research The course will cover foundations of Linear Algebra including vector spaces, linear transformations, matrices, linear systems, Hilbert and Banach spaces, orthogonally, projections and eigenvalues and eigenvectors. Next, the foundations of Mathematical Analysis will be covered including basics of sets and topology, continuity, differentiability, power series and Taylor series with special care to multivariable case. In addition, special attention will be devoted to convex sets and convex functions. Furthermore, time permitting; it will be illustrated how some of these mathematical concepts and methods are used in analyzing and developing certain optimization models and methods including interior-point methods. IENG610 Philosophy of Science, Engineering and OR Engineering curricula contains many courses that dwell on a given area with instruction into the known and tested tools and techniques to address analysis and design. This course provides the doctoral student, who is expected to embark on a lifetime career of extending the boundaries of engineering, with a preliminary exposure to the philosophical foundations of the methods of modeling and reasoning. The objective is to raise the awareness of intent, limitations, purpose, and validity as they relate to engineering research. IENG641 Nonlinear Programming This course covers the various theoretical aspects of nonlinear and convex optimization. Optimality conditions, along with convex sets, convex functions, and related topological properties are covered at an advanced graduate level.
IENG642 Large Scale Optimization This is an applications-oriented course on the modeling of large-scale systems in decisionmaking domains and the optimization of such systems using state-of-the-art optimization tools. The mathematical structure of the problem will be explored as well as the special solution techniques will be discussed. The course will cover a broad overview of the applications, basic theory, and decomposition methods of this vibrant field. Emphasis in this course is placed on both theory and practical algorithm implementation and tools for solving large size optimization problems. Methods include decomposition-coordination algorithms for large-scale mathematical programming such as Benders, regularized Benders, Dantzig- Wolfe and Lagrangian relaxation. IENG647 Multi-Criteria Optimization In engineering problems having more than one objective is quite common. Most of the times these objectives conflict with each other. Thus a solution optimizes one of the objective functions may not be the best solution for the rest of the objective functions. Therefore instead of a single optimum solution a set of Pareto optimum solutions should be provided to decision-makers. The course provides an introduction to the theory and practice to multicriteria optimization, which is one of the most effective mathematical tools in mathematics and operations research. The topics covered in the course include problem formulation, properties of solutions, algorithmic solution approaches, and applications of multiple criteria decision-making (MCDM). IENG650 Discrete Optimization The course offers a general understanding of, appreciation for, and experience in the methods and techniques involved in discrete optimization. Both exact and approximate solution techniques are covered. Topics include optimization, relaxation, bounded optimization, network flows, branch and bound techniques, dynamic programming, cutting plane algorithms, and approximations. IENG651 Network Optimization Network flow problems are special cases of linear programming problems. They occur in various industrial engineering applications such as transportation, logistics, production and manufacturing, computer networks, and project management. This course covers both the fundamental theory of network flows and its various applications in a diverse set of fields. IENG662 Applied Stochastic Processes ll The material is a continuation of that of IENG561 Applied Stochastic Processes I. It contains the more advanced stochastic processes including the renewal process, Brownian motion, and the Gauss process. In addition the course covers reliability theory. The approach combines the study and applications of the models along with the development and cultivation of a general understanding of modeling. Students after successfully completing the course will be able to develop stochastic models and apply them to industrial engineering systems. IENG664 Dynamic Programming The course studies techniques for optimal control of dynamical systems which evolve over time. Although the emphasis is on discrete-time models, continuous-time models are also given. The Bellman equation and dynamic programming algorithms, and their applications
will be covered. The focus may shift from semester to semester to different industrial engineering applications. IENG668 Stochastic Programming The field of stochastic programming is currently developing rapidly with contributions from many disciplines such as operations research, mathematics, and probability. The course will cover a broad overview of the applications, basic theory, and decomposition methods of this vibrant field. Methods include decomposition-coordination algorithms L-shaped method and statistically motivated decomposition methods. Elective Application Area Courses IENG525 Sequencing and Scheduling Theory Scheduling tasks seen in both manufacturing and service industries are introduced. The course emphasizes a systems view approach to scheduling. Scheduling is an inherently difficult and computationally intensive task. Issues involved in scheduling are discussed. Starting with the fundamental principles, complex algorithms and computer-aided scheduling are presented. The various solution approaches and techniques are covered. Single and parallel machine scheduling, job shop, assembly line, flexible manufacturing, and workforce scheduling are studied. IENG570 Supply Chain Processes and Management Elements of supply chains, distribution networks in supply chains and network design, inventory management in supply chains, aggregate planning, value of information and coordination in supply chains. IENG571 Logistics and Transportation Systems The class will cover quantitative techniques of Operations Research with emphasis on applications in logistics and transportation systems analysis (urban, air, ocean, highway, pick-up and delivery systems). It presents inventory management, logistics network design, analysis and its strategy. IENG572 Design and Operations of Distribution Centers This course covers the topics such as material handling systems, dock operations and management, put-away operation and storage policies, order picking policies and methods and warehouse designing. Additionally, container loading and pallet loading models are being covered. IENG575 Analysis of Inventory Systems This course aims to develop an advanced level of knowledge on mathematical inventory theory. It is aimed to largely focus on single-item, single-echelon systems but models for the systems with several products, locations and customer classes will be also developed. Elements of inventory systems, single-item single-location deterministic and stochastic models, models with several items, locations or customer classes.
IENG576 Revenue and Pricing Management This course aims to provide a comprehensive introduction to revenue management that focuses on the demand-side decisions such as the understanding of the demand drivers, segmentation and pricing strategies. Price-response functions, price-response estimation, price differentiation, segmentation, capacity allocation, network management, overbooking, auctions and selected topics from other pricing contexts. IENG577 Design and Analysis of Production Systems This course uses industrial engineering methods and techniques to design, plan, and analyze production systems. The main topics comprise group technology and cellular manufacturing, just-in-time manufacturing, and optimization strategies for discrete job-shop manufacturing. In addition, materials handling and inventory systems and their interrelations, and the integration of manufacturing systems are discussed. Stochastic and deterministic optimization methods and heuristics are studied. IENG665 Queueing Systems This course provides a general framework to model queueing systems and their analysis. Fundamental queueing systems will be reviewed and their properties mathematically developed. Upon the successful completion of the course, the student will be familiar with the basic queueing theory models, and have acquired the skills to model manufacturing and service systems using these models. Further, when the need arises, the student will be able to develop specialized queueing systems and analytical solutions for special custom applications. IENG666 Performance Analysis of Manufacturing Systems Stochastic models of a variety of manufacturing systems (flow lines, job shop, transfer lines, flexible manufacturing systems, flexible assembly systems, cellular manufacturing) are studied. The developed models are then used to analyze the performance of such manufacturing systems, study the design issues, develop policies for the control of the manufacturing systems, and establish the interactions among the various manufacturing systems components. IENG691 Advanced Topics in IE and OR Theoretical and practical aspects of the study and development of advanced operations research models, and the application or implementation of these models to the general fields of industrial engineering. The course heavily dwells on the new developments in the literature of industrial engineering and operations research. Recent developments that represent the state-of-the art will be studied through recent additions to the literature. Specific topics may differ from semester to semester according to the needs and interests of the student group. IENG599 Thesis Master's Thesis Study (Industrial Engineering) The student studies a personalized research topic and makes contributions to the field. The thesis is supervised by a thesis advisor. The student periodically presents her progress to the research advisor.