History and development of Industrial and Systems Engineering. Job areas for Industrial and Systems Engineers. Basic Industrial and Systems Engineering concepts. Steps of the system design process. Introduction to mathematical modelling and an overview of Operations Research models. Introduction to various Industrial and Systems Engineering subjects will be performed by guest speakers and department members whenever possible. Basics of MS […]
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.
The aim of this course is to improve programming skills of students and apply these skills to the problems encountered in the field of Industrial and Systems Engineering. Topics covered are: Review of programming skills: variables, matrices, basic arithmetic operations, modular programming with functions, program control and loops, algorithms with single and nested loops.Computer implementation of the following methods which […]
Descriptive statistics, random sampling, sampling distributions, point estimatiom, confidence intervals, hypothesis testing, goodness-of-fit tests, tests for independence, linear regression and correlation, statistical package applications.
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 […]
Introduction to database systems; data modeling and entity relationship (E/R) diagrams; relational databases; structured query language (SQL); HTML and CSS programming; design of interactive web sites using server-side programming.
This is an introductory course designed to introduce the basic concepts and properties of modeling and analysis of probabilistic systems and covers the following topics. Probability Theory Review: 1. Probability Theory basics including discrete and continuous Random Variables, Conditional Probability and Conditional Expectations. 2. Markov Chains: Basics, Chapman-Kolmogorov Equations, Limiting Probabilities and applications including Absorbing Chains, Work-Force Planning Models. 3. […]
Principles of accounting and cost accounting systems, financial tables, various product costing techniques. Cost estimation methods. Evaluation of alternative investment projects, cost-benefit analysis and examples financial decision making problems.
Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction techniques, random number and variate generation, Monte Carlo simulation. Course content is supported by simulation packages.
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, […]
Introduction to supply chains. Flexible models in aggregate planning, optimization and heuristics. Continuous and periodic review models, optimizing inventory model parameters, simulation. Distribution resource planning, lot sizing techniques, Material requirements planning. Supply chain network design, supplier selection models, ERP software.
System Dynamics in Engineering. Linear, Non-Linear and Complex Systems. Modeling of Technical and non-Technical Systems. Lotka-Volterra logistics equations. State-Space representation. Causal loop diagrams. Positive and negative feedback loops. Introduction to STELLA programming and system analysis using STELLA. Generic flow process. Stock management structure. Material and Information delay. Class group projects.
This course is designed to help students utilize their knowledge on system engineering methodologies to engineer design and develop complex systems and products. Topics include integration of user needs, defining technological opportunities, financial and schedule constraints to build complex systems, and basic tools and techniques to manage complex engineering projects.
This course aims to introduce the interaction between topics of statistics, data analysis and machine learning with modern data science tools, and to cover the entire process of data science starting from data manipulation to the development of learning algorithms. The topics covered are: Introduction to R programming, data manipulation, missing data analysis, exploratory data analysis and data visualization, inferential […]
The purpose of this course is to provide an in depth coverage and applications of Decision Making to complex real-world problems. The course covers: 1. Modeling of the Decision Problems : This module is composed of introductory concepts such as ; Elements of Decision problems, Structuring Decisions and Sensitivity Analysis 2. Modeling Uncertainty : Uncertainty module covers Probability review, Probability […]
A statistical approach to manufacturing quality control; construction and interpretation of various control charts, process capability studies, specification and tolerances, acceptance sampling; cost aspects of quality decisions; principles of Total Quality Management ; quality improvement programs.
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)
Basic definitions, trees, connectedness, Eulerian and Hamiltonian graphs, matchings, edge and vertex colouring, chromatic numbers, planar graphs, directed graphs, networks.
Introduction to the fundamental computational methods used in Industrial and Systems Engineering, with particular emphasis on optimization, numerical analysis, network flows, location and inventory control. Course content is supported by hands on experience with Python language.
The aim of this course is to provide students an understanding of the principles and techniques used in the improvement and measurement of productivity, as well as concepts of work study and their application to the human-machine system design and interaction. The objective is to equip students with the ability to use and apply the techniques of method study, work […]
Classification of scheduling models, performance criteria, concept of active schedules, single machine, flow shop and job-shop problems, parallel processor problems, constructive algorithms in scheduling, combinatorial optimization approaches.
This course will provide a broad coverage of ergonomics (human factors) and show how the application of ergonomics (human factors) principles can improve the design of systems involving the interaction of humans with technology. The course includes a broad based introduction to ergonomic principles and their application in the design of work, equipment and the workplace. Consideration is given to […]
Applications of nonlinear programming. Single variable optimization, multi-variable unconstrained optimization, equality constrained optimization (Lagrangian method) and inequality constrained optimization (KKT conditions) problems and iterative solution methods. Heuristic methods, modeling languages and optimization software.
Fundamental concepts in the planning and design of logistics systems. Logistics network design, warehouse design and operation, planning and managing long and short haul transportation. Emphasis on operations research models that address the operational, tactical and strategic decisions in logistics and solution methodologies employed in their solution.
Mathematical models employed in the solution of Operations research problems including lineer, nonlinear, integer and Marcov chain models; application of these models to areas such as production, financial planning and distribution; complexity of the resulting mathematical models and techniques used in their solution.
The students will understand the principles, techniques and practices of Managing Innovation and Entrepreneurship. To achieve these goals the course is divided into four modules. The first three modules cover the following three areas: exploring, executing and exploiting innovations. The fourth module is renewing innovation and examines how established firms re-energize their exploration, execution and exploitation practices to renew the innovation […]
Minimum cost network flows, maximal flow, shortest path; emphasis on applications in industrial and service sectors, generalized networks. Project management topics including CPM, PERT, time-cost trade off, resource constrained project scheduling and financial analysis of projects.
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.
Naive methods, moving average methods, exponential smoothing (simple, Holt’s, Winters’), classical time series decomposition, regression methods, Box-Jenkins ARIMA models, statistical package applications.
This course is an introduction to the fundamental methods used in Computer Integrated Manufacturing. Topics covered will include computer aided design, geometric tolerance, process engineering, tooling and fixtures, data communication in manufacturing and robotics. CNC lathe and milling machine programming, robot programming, PLC programming will also be covered. Also, a project on a recent CIM topic will be performed by […]
General outlook of Process Automation System. Hierarchical layers of Automation. Measurement systems. Static and dynamic characteristics of instruments and actuators. Principles of the measurement of temperature, pressure and flow. PLC and DCS based process automation. Instrument and control networks. Human Machine Interface. Process automation projects from systems engineering perspective. Student term projects and presentations.