Graduate Courses
Credit Information: (3+0+0) 3
Description:
Linear programming modeling; linear algebra, convex analysis, polyhedral sets; simplex method; modeling with GAMS; algorithmic complexity; the computational efficiency of the simplex method; efficient simplex implementations; duality and the sensivity analysis; decomposition principle; computational complexity; the complexity of linear programming; interior point methods; introduction to convex programming.
Credit Information: (3+0+0) 3
Description:
Integer programming; cutting plane, branch and bound methods; Lagrangean relaxation and subgradient optimization; optimality conditions for nonlinear programming; basic algorithms for unconstrained and constrained nonlinear programming; dynamic programming.
Credit Information: (3+0+0) 3
Description:
Investment decisions; facility location, capacity, layout; aggregate planning and master scheduling; inventory control; material requirements planning; production scheduling and control; project planning and control; quality control; maintenance planning.
Credit Information: (3+0+0) 3
Description:
Basic probability theory: Sample space, events, probability, and conditional probability. Discrete and continuous random variables; marginal, joint and conditional distributions; expectations and conditional expectations. Inferential statistics: Sampling theory; parameter estimation, point and interval estimation and hypothesis testing. Applications in industrial engineering and operations research using statistical software packages.
Credit Information: (3+0+0) 3
Description:
Random variables and stochastic processes: Generating functions, Bernouilli and Branching processes, Poisson processes and applications in traffic models. Renewal and regenerative processes and applications in inventory control and reliability models. Markov chains and Markov processes with applications in queueing models. Introduction to Brownian motion with financial applications.
Prerequisite: Consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Design of experiments methodology; simple comparative experiments; single factor experiments; randomized blocks; Latin square designs; factorial designs; fractional factorial designs; regression models; response surface methodology; random effects models; nested and split plot designs; robust designs; mixture designs; optimal designs.
Credit Information: (3+0+1) 3
Description:
Estimation Theory, sufficiency, maximum likelihood estimation, interval estimates, and hypothesis testing, Neyman-Pearson approach, likelihood ratio test, linear statistical models (regression and ANOVA), generalized linear models, and logistic regression. Applications in industrial engineering and operations research.
Prerequisite: Consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Simulation methodology, model formulation, systems dynamics, overview of simulation languages, generating random varieties, output data analysis, model validation, variance reduction techniques, experimental design and optimization.
Credit Information: (3+0+0) 3
Description:
Convex analysis; necessary and sufficient conditions for optimality, methods of unconstrained optimization, necessary and sufficient conditions for constrained optimization, methods for handling equality and inequality constraints, nonlinear programming methods such as primal methods and penalty function methods.
Credit Information: (3+0+0) 3
Description:
Introduction to graph theory; graph search; data structures for graph and network flow algorithms; shortest path problems; minimum spanning tree problem; matching in bipartite graphs; maximum flow - minimum cut and minimum cost circulation problems.
Credit Information: (3+0+0) 3
Description:
Introduction to combinatorial optimization; shortest path problems; minimum spanning tree problem; maximum cardinality and weight matching problems in bipartite graphs; Hungarian algorithm; maximum cardinality and weight matching problems in general graphs; Edmond's algorithm; integral polyhedra; matroids; greedy algorithm.
Prerequisite: IE 501 or equivalent.
Credit Information: (3+0+0) 3
Description:
Introduction to heuristic methods; classical construction heuristics; classical improvement heuristics; Lagrangean relaxation, simulated annealing, tabu search. Neural networks, genetic algorithms, ant colony optimization.
Prerequisite: Consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Continuous and discrete space facility location models, and location/allocation models. Facility layout models and solution methods. Group technology and cellular manufacturing, cell formation using clustering, mathematical programming and other methods. Design of flexible manufacturing, warehousing, distribution and logistic systems.
Prerequisite: Consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Overview of production systems and planning paradigms. Hierarchical planning, aggregation/disaggregation. Continuous and discrete lot-sizing models and solution methods. Distributed planning and coordination in supply chains.
Prerequisite: Consent of the instructor.
Credit Information: (2+0+2) 3
Description:
In this course practical aspects, applications and implementation problems of mathematical programming models will be discussed and analyzed. In particular, linear programming, integer programming, and multiobjective programming models will be considered. For these models, application areas, underlying assumptions, special technical considerations, typical implementation problems will be investigated. Many case studies and discussion papers on the topic will be analyzed and discussed.
Credit Information: (2+0+2) 3
Description:
Information management for manufacturing enterprise integration with emphasis on concepts such as CIM and Concurrent Engineering, production management approaches such as MRP II, JIT and OPT, engineering functions such as CAD and CAPP, and Shop Floor Control. A development framework for an information system for Shop Floor Control: structured analysis for modelling information requirements, a reference model for system design; a review of information requirements, a reference model for system design; a review of information technology including state-of-the-art architectures and tools such as distributed systems, open systems, factory networks, communication standards, and database management systems.
Credit Information: (3+0+0) 3
Description:
Bayesian decision theory; measurement theory; subjective probability. Dependency models; Bayesian networks; exact and approximate inference; computational complexity of inference. Influence diagrams; value of information; decision networks and connections to Markov decision processes. Case studies; risk sharing and decisions; implementation of decision models.
Credit Information: (3+0+0) 3
Description:
Centralized and decentralized analysis of production and distribution systems. Existence and uniqueness of equilibrium in principal agent and simultaneous move games, information asymmetry, Bayesian games, cooperative games, dynamic games. Contract design, enforceability of contracts.
Prerequisite: Consent of instructor.
Credit Information: (3+0+0) 3
Description:
Essentials of queueing theory, Jackson networks, queueing networks with finite buffers. Machine failures, analysis and modelling of transfer lines, assembly/disassembly lines, quality failures. Control of production systems: Kanban, base stock, continuous work-in-process (CONWIP).
Prerequisite: IE 505 or consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Use of systems thinking and system dynamics modeling methodology in the analysis of complex, dynamic socio-economic and managerial problems. Lab experiments with simulation models of real case studies ranging from ecological to business issues, from social to agricultural problems. Basic methods and tools of dynamic feedback modeling: stock-flow and causal loop diagrams, linear and non-linear equation formulation and generic structures. Use of a modern modeling/simulation software such as STELLA, VENSIM, POWERSIM. Student term projects involving applied dynamic modeling.
Credit Information: (3+0+1) 3
Description:
Physics and physiology of humans at work; biomechanical and physiological modeling, neuromuscular performance, mechanical work capacity; methods to improve work performance, health and safety, workplace and equipment design, shiftwork and rest allocation; cognitive workload, worker selection and training; controlling environmental stress; bioinstrumentation. Special emphasis given to learning about work capacity measurements, instrumentation, and laboratory experimentation.
Prerequisite: Consent of the instructor.
Credit Information: (0+1+0) Non-Credit
Description:
Seminar on recent and contemporary topics in industrial engineering, operations research, and related fields; presentations and discussions designed to fit the academic interests of the faculty as well as current issues in theory and practice.
Credit Information: (0+1+0) Non-Credit
Description:
The widening of students' prespectives and awareness of topics of interest to industrial engineers through seminars offered by faculty, guest speakers and graduate students.
Credit Information: (3+0+0) 3
Description:
Current topics of interest in Industrial Engineering selected to suit both the class and the instructor.
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:
The philosophy and fundamental concepts of systems theory, various mathematical and quasi-mathematical techniques for dynamic feedback modeling and analysis. Notions of equilibrium, stability and major types of non-linear dynamics; shift of loop dominance, path-dependence, limit cycles, multiple periods, bifurcations. Examples from socio-economic, managerial and other living systems. Suitable simulation/modeling software, spesifically for large-scale non-linear models. Student term project.
Prerequisite: IE 550 or consent of the instructor.
Credit Information: (3+0+0) 3
Description:
Limiting behavior and potentials of Markov chains; Markov processes and infinitesimal generators; renewal theory and regenerative processes; Markov renewal processes; Brownian motion and its sample path analysis.
Prerequisite: IE 505 or instructor's consent.
Credit Information: (3+0+0) 3
Description:
Order statistics and related distributions; sufficiency and related theorems; point estimation, criteria for selecting estimators, methods of estimation; Neyman Pearson theory; likelihood ratio tests; Bayes and minimax procedures; sequential procedures; confidence estimation; general linear hypothesis; analysis of variance; non-parametric statistical inference.
Prerequisite: IE 508 or instructor's consent.
Credit Information: (3+0+0) 3
Description:
Modeling with integer variables; polyhedral combinatorics; theory of valid inequalities; disjunctive programming; duality and relaxation; linear programming relaxation; enumeration; branch-and-bound using linear programming relaxations; cutting plane algorithms; Lagrangean relaxation and duality; sub-gradient method; column generation technique; reformulation and linearization technique; lift-and-project method; problems with special structure.
Prerequisite: IE 501 or instructor's consent.
Credit Information: (3+0+0) 3
Description:
Multi-stage problem solving; several state variables; recursive equations; principle of optimality; computational aspects; decomposition in dynamic programming and uncertainty; non-serial systems; dynamic programming and decision processes.
Prerequisite: IE 501 or instructor's consent.
Credit Information: (3+0+0) 3
Description:
Decomposition, partitioning and compact inverse methods to deal with large and sparse optimization. Special structures such as Leontief substitution systems, production-inventory models. Simplex method with upper bounds and generalized upper bounding. Constraint relaxation methods. Branch and bound and Bender's partitioning methods to solve mixed integer linear programs.
Prerequisite: IE 501 or instructor's consent.
