Master Of Science Course Descriptions
Linear programming and extensions: formulation and solution of linear programming models, Simplex algorithm, sensitivity analysis, duality, transportation, assignment and network problems, introductory integer programming. Course content is supported by optimization software.
This course introduces the systems engineering design and integration process, including the development of functional, physical, and operational architectures. The emphasis of this course is on requirements engineering, functional modeling for design, formulation and analysis of physical design alternatives, verification and validation. The course is designed to provide students with experience using mathematical and graphical tools for systems analysis and […]
Systems classification and introduction to Dynamic Systems. Mathematical modeling. ODE solution methods and comparison from systems dynamics perspective. Laplace transforms. Transfer function and system’s response analysis. Stability Analysis (R-H Criteria). Feedback concept and feedback control. Closed loop response. System analysis and design using Root-Locus method. Analysis of transportation-lag in the loop. Introduction to non-linear systems.
Introduction to work and time study, work analysis, learning curve, man-machine systems, elements of production and service system design, layout types and methods, location models, managerial and planning distinctions. Planning flow shops and job shops, cellular manfacturing, manual and automated assembly lines, automated production lines, flexible manufacturing systems. Critical Path methods, PERT and resource allocation in project management. Forecasting methods, […]
Statistical experiments and events. Set theory. Interpretations and axioms of probability. Basic theorems of probability. Counting techniques. Independence of events. Conditional probability. Bayes’ theorem. Discrete distributions (binomial, hypergeometric, geometric, negative binomial, Poisson). Expectation and variance. Continuous distributions (uniform, normal, exponential, gamma, lognormal). Joint, marginal and conditional distributions. Conditional expectation and variance. Covariance and correlation. (ECTS: 6)
This course includes analytic and descriptive survey designs, pilot work, selection methods for data collection, evidence interpretation and reporting methods as preliminary to a research project to be prepared by the student under the surveillance of a faculty member. The project provides an opportunity to prepare and present an integrated technology oriented project of significant importance to the student’s organization […]
The aim in this course is to introduce systems engineering methodology and some of its applications to graduate students. The subject matter will be treated through analytical tools, wherever appropriate, and will cover the following material: introductory concepts; systems thinking; system life cycle; system modeling for analysis and design; life cycle costing; system realibility and effectiveness ; solution design; solution […]
Convex analysis, polyhedral sets, the simplex method, duality and sensitivity analysis, decomposition principle, complexity of the simplex algorithm and polynomial algorithms, minimal course network flows, transportation and assignment problems, out-of-kilter algorithm, maximal flow, shortest path, multicommodity flow problems.
Introduction to state-space models.The relationship between transfer function and state-space model. Definition of observability and controllability. Pole-placement. State feedback vs output feedback. Observer design. Lyapunov stability criteria. Optimal Control based on state-space. Model reduction.
Two-sample tests, one-way analysis of variance, randomized block designs, factorial designs, two-way anova, 2k factorial designs, random effects, mixed effects, simultaneous confidence intervals, EMS, power computations, statistical package applications. Prerequisite: A background in statistics at the level of ISE 254.
Logistics Network Design (Single/Multi Echelon Facility Location Models, Location Covering, p-Center Models, …), Design and Management of a Warehouse (Warehouse Sizing and Layout, Storage Medium Selection, Batch Formation, Order Picking and Packing), Long Haul Freight Transportation Models, Short Haul Transportation Models.
Basics of fuzzy set theory. The concept of fuzziness and linguistic variables, membership functions. Fundamental application areas of fuzzy set theory: Fuzzy logic, Expert Sytems, Fuzzy Decision making, Fuzzy Clustering, Fuzzy models in OR.
A graduate level introduction to the fundamental ideas in optimization. Mathematical modeling in deterministic and non-deterministic settings. Topics in the theory and application of mathematical optimization, network analysis, decision theory and stochastic processes are also included.
Specification of criteria and objectives for complex decisions. Determination of alternatives and decision-making process. Multi attribute utility theory. Analysis of selected multi attribute decision making methods and fuzzy multi attribute decision making models. Goal programming and introduction to multi objective decision making.
This course covers the basics of decision making process and various approaches utilized in this process. The decision process approaches are exemplified by student presentations.
Statistical models for stock prices, basics of simulation and R, Options, Monte Carlo methods and variance reduction techniques, Option pricing by simulation, Pricing exotic options, Binomial trees, Quantifying the risk of stock portfolios: Market and Credit Risk, Mortgage Backed Securities, Interest rate models and Bond pricing, and Final Project.