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Endüstri Mühendisliği Yüksek Lisans Programı

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  • Program tanımları
    ENDÜSTRİ MÜHENDİSLİĞİ YÜKSEK LİSANS PROGRAMI

    Programın Amacı

    Yüksek Lisans Programı değişik mühendislik ve fen bilimleri disiplinlerinden gelen öğrencilere Endüstri Mühendisliği bilgi, teknik ve yaklaşımlarını aktararak onları akademik kariyere yönlendirmeyi veya çağdaş yönetim bilgi ve teknikleriyle donanmış yöneticiler olarak yetiştirmeyi amaçlar.

    Fakültenin Avrupa Kalite Ödülü Finalisti olmasında öncü rol oynayan Bölüm öğretim üyeleri Kalite Yönetimi ve  Sürekli Kurum İyileştirme alanlarındaki teori ve pratiğe dayalı birikimlerini öğrencileriyle, sanayi ve toplum ile araştırma projeleri, tez çalışmaları, danışmanlık ve eğitim programları aracılığı ile paylaşmayı hedeflemiştir.

    Programın Dili : İngilizce

    Bilimsel Hazırlık Programı Gerektiren Bilim Alan ve Dalları
    Lisans Eğitimini farklı bir mühendislik dalında tamamlayan öğrencilere Bilimsel Hazırlık Programı uygulanır. Bilimsel Hazırlık Programı’ndan 3 den fazla ders alma gereksinimi olanlar Bilimsel Hazırlık Programına katılmak zorundadır. Bilimsel Hazırlık Programında 2 yarıyılda toplam 30 kredi saatinden fazlasını gerektiren dallardan mezun olmuş öğrenciler lisansüstü programına alınmazlar.

    DERS İÇERİKLERİ

    QUALITY ENGINEERING
    TQM, principles of quality control systems,process capability, control charts for variables, control charts for attributes, EWMA, CUSUM, acceptance sampling plans, basic quality management tools

    DESIGN OF EXPERIMENTS
    Basic statistical concepts, simple comparative experiments, Analysis of Variance (ANOVA), Experiments to compare several treatments (one-way ANOVA), Blocking and nuisance factors (two-way ANOVA), Factorial and fractional factorial experiments, Taguchi’s contribution to quality engineering, Regression analysis and analysis of covariance, Response surface methodology.

    ERGONOMICS
    This course will study human capabilities that are pertinent to the design of products, equipment, work tasks, and environments. In addition, this course will provide students with information on design principals that should be incorporated to maximize human performance, safety, and product quality. Upon completion of this course, students should be knowledgeable of design strategies, principals, and techniques that can be used to minimize human error, injuries, discomfort, and dissatisfaction.

    MATHEMATICAL PROGRAMMING

    The objective of the course is to help students understand the characteristics of linear programming, integer programming and network flow problems, as well as non linear models.The course also focuses on the mathematical principles underlying the simplex method.The students model verbal problems in GAMS language and solve them.

    INFORMATION PROCESSING SYSTEMS
    Organization of major types of information processing systems. Programming languages (C, Pascal) Database management sytems. Alternative system organizations. Techniques for evaluation of performance of systems.

    ADVANCED PRODUCTION PLANNING AND CONTROL
    The introduction covers the definition and classification of production systems and the levels of decisions taken to manage production. Next, decision problems which are included in strategic planning, i.e., product and facility design, single and multi-facility location problems, are discussed, and solution techniques are introduced.   Finally, aggregate planning models used in tactical planning are discussed along with inventory management models, including EQS models, models with probabilistic and/or deterministic but varying demand, periodic and continuous review systems and their suitability to various environments.

    COMPUTER INTEGRATED MANUFACTURING
    An introduction to computer-integrated design and manufacturing with a focus on manufacturing process planning. Emphasis on concurrent engineering principles, manufacturing process engineering, computer-aided process planning, NC programming, and CAD/CAM integration. Course provides experience with CAD/CAM software and NC machines.

    STATISTICAL DECISION MAKING
    Complex decision making and evaluation problems typically involve trade-offs on multiple objectives or criteria that are often conflicting with each other. Decision makers having to perform these trade-offs use their judgements about their preferences for possible consequences of alternatives or alternative courses of action. This course deals with the mathematical theory and real-world practice of developing multi attribute value and utility models to represent these highly qualitative judgments.

    FINANCIAL ANALYSIS
    The objective of this course is to provide the tools and knowledge necessary for an examination of an organization's financial condition. We will accomplish this objective by reviewing financial statements, conducting financial analysis, and investigating issues of earnings quality.

    MANUFACTURING STRATEGIES
    The objective of the course is to help students understand the characteristics of linear programming, integer programming and network flow problems, as well as nonlinear models. The course also focuses on the mathematical principles underlying the simplex method. The students model verbal problems in GAMS language and solve them.

    FORECASTING AND TIME SERIES ANALYSIS
    Demand Patterns and Filtering, Horizontal Models, Trend Models, Quadratic Models, Regression, Discounting, Adaptive Smoothing, Seasonal Models, Adaptive Control Models, Box-Jenkins Models, Special Techniques in Forecasting.

    PORTFOLIO MANAGEMENT
    The Investment Background, Introduction to Portfolio Management and Asset Pricing Models, An Introduction to Basic Principles of Financial Asset Management, Determination of Investment Objectives, Constraints, and Portfolio Policies, Valuation Principles, Practices and Expectations for Capital Markets, Integrating Expectational Factors and Portfolio Policies and Constructing Portfolios, Portfolio Management of Fixed-income and Equity Portfolios, Emerging Markets.

    SPECIAL TOPICS IN INDUSTRIAL ENGINEERING I
    Case studies of application of various Industrial Engineering problems such as production planning, quality control, facility layout, simulation, and other models.

    SPECIAL TOPICS IN OPERATIONS RESEARCH I
    Case studies of application of linear and nonlinear models and general types of search techniques.

    ADVANCED MANUFACTURING SYSTEMS
    Total manufacturing system, manufacturing strategy design, BOM, routing, work centers, production and resource planning, master scheduling, MRP and capacity management.

    NETWORK THEORY AND PROJECT SCHEDULING
    Network theory, graphs and their characteristics, transportation, assignment, shortest path, maximal flow, minimal cost network flow problems, theory and solutions, project scheduling, CPM/PERT, time cost trade-off, time resource trade-off problems, resource constrained project scheduling, combinatorial methods and heuristic.

    SIMULATION MODELING OF PRODUCTION AND SERVICE SYSTEMS
    Use of simulation in the analysis and design of systems involving continuous and discrete manufacturing processes; simulation planning of computer integrated manufacturing planning related to robotics, flexible, and integrated manufacturing systems. Analysis and design of service systems. Use of computer graphics combined with simulation analysis for manufacturing systems decision support.

    DESIGN AND ANALYSIS OF MANUFACTURING SYSTEMS
    Major modeling techniques such as analytical, algorithmic, simulation, AI, and Petri-nets. Modeling manufacturing systems: layout, performance modeling, scheduling, tool management, process control. A prototype system modeling using one of these popular modeling techniques and analysis of the performence using major performance measures.

    STOCHASTIC PROCESSES
    Random variables, stochastic processes, birth and death proceses, Bernoulli processes, Poisson processes, Continuous and discrete time Markov chains with finite and infinite number of states, and applications to reliability and quality control. Markov processes, renewal theory, regenerative processes, and queueing theory.

    SCHEDULING THEORY I
    The course starts with the classification of scheduling problems, the performance criteria used in scheduling, disjunctive graph presentation of schedules, and the definition of optimality and activeness. Single machine problems, flow-shops and job-shops and related proofs and solution techniques. Constructive algorithms, optimization and combined solution approaches are discussed. The course comes to an end by covering the resource constrained project-scheduling problem which is the most generalized model in scheduling. Time-cost and time-duration trade-offs, renewable resource constraints and the recent trends in project scheduling research are discussed.

    MULTIVARIATE STATISTICAL ANALYSIS
    General Purpose and Description, Matrix Algebra, The Multivariate Normal Distribution, Principal Components, Reliability and Scale development, Factor Analysis, Discriminant Analysis and Logistic Regression, Clustering, Multidimensional Scaling and Correspondence Analysis

    COMPUTER AIDED DESIGN
    Graphics devices and fundamentals of operation. Two-and three-dimensional transformations. Interactive graphical techniques and applications. Three dimensional graphics, perspective transformation, hidden line elimination. Data structures and languages for graphics. Interactive graphical programming.

    DYNAMIC PROGRAMMING
    The techniques of recursive optimization and their use in solving multisatge decision problems, applications to various types of problems. Algorithms for solving Markovian programming problems and their applications.

    FLEXIBLE MANUFACTURING SYSTEMS
    Advanced manufacturing systems concepts. Step by step design of flexible manufacturing systems; system elements - CNC machines, material handling systems, pallets-fixtures, other auxillary exuipment, computer control and communication networks; tool management systems in FMS; modeling of systems by simulation and network-of-queues.   Economic justification of advanced manufacturing systems.

    INDUSTRIAL ROBOTICS
    Robot structures and classification; drive system technology; control systems; robot programming and programming langugages; robot tooling and industrial application of robots including spot and arc welding, assembly, handling and palleting machine loading, surface coating etc.

    INTEGER PROGRAMMING

    Modeling with integer variables, total unimodularity, cutting plane approaches, branch-and-bound methods, Lagrangean relaxation, Bender’s decomposition, the knapsack, and other special problems.

    INVENTORY MANAGEMENT

    Review of basic inventory models. Models and solution techniques in various problems related to multi-stage production and distribution systems. Topics include: assembly systems, material requirements planning, hierarchical production planning, flexible manufacturing sytems, distribution systems. Readings will include classic works and recents papers on techniques and applications.

    RELIABILITY AND QUALITY ASSURANCE SYSTEMS
    Total quality management; concepts and practice. Design and implementation of the TQM campaign. TQM in world class manufacturing. Quality assurence systems: the quality manual, supplier quality assurance, quality audits, performance measures, information systems, registration and certification. ISO 9000. Quality assurance services. Quality in design; design approval, contract review, tendering, design assessment. Quality philosophies; Kaizen, zero defects, quality costing.

    DATA MINING

    Data Mining and Knowledge Discovery in Databases, Data Mining Techniques, Market Basket Analysis, Link Analysis, Decision Trees, Artificial Neural Networks, Genetic Algorithms, OLAP

    SPECIAL TOPICS IN OPERATIONS RESEARCH II
    Case studies of application of linear and non-linear models and general types of search techniques.

    SPECIAL TOPICS IN INDUSTRIAL ENGINEERING II
    Case studies of application of various Industrial Engineering problems such as production planning, quality control, facility layout, simulation, and other models.

    MATHEMATICAL STATISTICS
    Review of probability concepts. Random variables and their distributions. Moments, moment generating functions. Stochastic independence. Basic limit theorems. Transformations of random variables. Point and interval estimation. Hypothesis testing. Analysis of variance. Regression. Nonparametric methods.

    ADVANCED QUALITY ENGINEERING
    The Overview of The Quality Tools and Methods, Modeling Process Quality and Statistical Process Control, Control Charts for Variables, Control Charts for Attributes, Process Capability Analysis, Gauge Capability, Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA)   Control Charts, Multivariate Quality Control, Process Design and Improvement   With Designed   Experiments, Acceptance Sampling, The Other Quality Issues ( Quality Management Tools, QFD).

    ARTIFICIAL INTELLIGENCE SUPPORTED DECISION MAKING
    Principles of knowledge-based search techniques; automatic deduction; knowledge representation using predicate logic, semantic networks, connectionist networks, frames, rules; Applications in problem solving, expert systems, game playing, vision, natural language understanding, learning, robotics; AI programming languages.

    RESEARCH TOPICS IN INDUSTRIAL ENGINEERING I

    Individual research on current topics of IE.

    GAME THEORY
    The basic non-cooperative notions of dominance, Nash equilibrium, correlated equilibrium, sub-game perfection, trembling hand perfection and sequentiality.

    COMBINATORIAL OPTIMIZATION
    Contemporary techniques for key combinatorial optimization problems such as: shortest path, maximum flow problems, and the traveling salesman problem. Techniques include problem-specific methods and general approaches such as branch-and-bound, genetic algorithms, simulated annealing, and neural networks.

    ADVANCED BAYESIAN DECISION ANALYSIS
    Axiomatic foundations for personal probability and utility; interpretation and assessment of personal probability and utility; formulation of Bayesian decision problems; risk functions, admissibility; likelihood principle and properties of likelihood functions; natural conjugate prior distribution; improper and finitely additive prior distributions; examples of posterior distributions, including the general regression model and contingency tables; Bayesian credible intervals and hypothesis tests; applications to variety of decision making situations

    METHODOLOGY OF OPERATIONS RESEARCH
    Formulation and modeling of applications from computer sciences, operations research, business, science and engineering involving optimization and equilibrium models. Survey and appropriate usage of software tools for solving such problems, including modeling language use, automatic differentiation, subroutine libraries.

    COMPUTER AIDED MANUFACTURING
    Emphasis on concurrent engineering principles, manufacturing process engineering, computer-aided process planning, NC programming, and CAD/CAM integration. Course provides experience with CAD/CAM software and NC machines.

    ADVANCED LINEAR OPTIMIZATION
    Objectives of this course are: to gain an insight on how the linear systems and simplex method work, to understand duality and dual simplex method, to solve certain non-linear systems using linear methods. MATLAB software will be used outside the class extensively.

    ADVANCED MULTIVARIATE STATISTICAL ANALYSIS

    General Purpose and Description, Random Vectors, Multivariate Sample Geometry and Random Sampling, The Multivariate Normal Distribution, Principal Components and Factor Analysis, Structural Equation Modeling, Canonical Correlation Analysis, Discriminant Analysis and Logistic Regression, Clustering, Multidimensional Scaling and Correspondence Analysis

    RESEARCH TOPICS IN INDUSTRIAL ENGINEERING II
    Individual research on current topics of IE.

    NON-LINEAR OPTIMIZATION
    Properties of convex sets in finite-dimensional spaces. Formulation of nonlinear programming problems. Saddle point (Lagrangian) optimality criteria for convex nonlinear programs. Duality theorems for convex programs. First- and second-order Kuhn-Tucker stationary-point theory for differentiable non-convex programs. Perturbation and sensitivity analysis. Applications and extensions.

    INVENTORY THEORY
    Analysis of inventory systems. General stochastic formulations. Markovian decision processes.

    STOCHASTIC PROCESSES IN DECISION MAKING
    Markov chains: classification, recurrence, transcience, limit theory. Renewal theory, Markov processes, birth- death processes. Reliability theory; coherent systems and reliability bounds. Markovian queues and Jackson networks.




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