Program tanımlarıENDÜSTRİ MÜHENDİSLİĞİ DOKTORA PROGRAMI
Programın Amacı
Doktora Programı, değişik mühendislik disiplinlerinden gelen öğrencileri Endüstri Mühendisliği bilgi, teknik ve yaklaşımlarıyla donatıp onları yaratıcı, araştırmacı, sorgulayıcı, modelleyici bilim adamları olarak yetiştirmeyi amaçlar.
Ders İçerikleri
QUEUEING THEORY
Waiting line costs, characteristics of a queuing system, single-channel queuing model, multiple-channel queuing models, constant service time model, finite population model.
ADVANCED SUPPLY CHAIN MANAGEMENT
Introduction to Supply Chain Management, Competitive and Supply Chain Strategies. Coordinated Product and Supply Chain Design , Supply Chain Integration, Customer Value and Supply Chain Management , Information Technology for Supply Chain Management, Pricing and Revenue Management in the Supply Chain, Reverse Logistics Issues in Supply Chain Management, Case Studies.
ADVANCED FORECASTING TECHNIQUES
Understanding Forecasting, Forecasting methods versus Forecasting Systems; Dynamic Bayesian Modelling; Methodological Forecasting and Analysis (prior and posterior, forward intervention, smoothing, component forms); Polynomial, Seasonal, Harmonic and Regression Systems; Superpositioning (Block structured Models, Block Discounting, Component Intervention); Variance Learning; Forecast Monitoring and applications; Time Series Analysis and Forecasting; BATS (Bayesian Analysis of Time Series software); Seasonal and Non Seasonal Box-Jenkins Models; Winters’ Exponential Smoothing; Decomposition Models. Term project will be demanded from students.
TOOLS AND TECHNIQUES FOR OPTIMIZATION
Introduction to modeling: the development cycle, interacting with clients, presenting results,basic model classes; linking them together; modeling language concepts, using modeling languages to build practical models; obtaining and manipulating input data; analysis and visualizion of results; integrating tools and software; WEB based optimization.
ADVANCED DATA MINING
Data Mining and Knowledge Discovery in Databases, Data Mining Techniques, Traditional Methods: Classification, Clustering, Data Adaptive Methods:Tree Structured Methods, Neural Network- Based Algoritms, Web Mining, Text Mining, Spatial Mining.
COMPUTATIONAL COMPLEXITY
Sets, relations, languages. Elements of Automata Theory: Finite Automata. Regular Languages and Regular Expressions. Context-Free Languages. Deterministic Turing Machines. Non-deterministic Turing Machines. Uncomputability. Decision Problems, Classes P and NP. NP-completeness: results and examples. NP completeness: more examples. NP-hardness. Approximate Algorithms. Random Algorithms.
GRAPH THEORY
Theory of graphs, including adjacency and incidence matrices, planarity, Hamiltonian circuits, Euler's formula, directed graphs, and trees. The efficiency of the known algorithms for performing various operations on graphs.