|
|
|
Course contents
|
|
| PHD in Industrial engineering & operations research is a degree intended for students with outstanding abilities and strong research interests, designed to prepare them for research careers in industry, government, or academia. The course work and research emphasizes the following four areas: production & operation management & optimization, mathematical programming and optimization, stochastic systems and simulation, dynamical systems and optimal control, and systems design and analysis. Students admitted to the PhD program typically are required to obtain, prior to their admission for candidacy, a master's degree requiring a research thesis. PhD candidates normally take approximately eight courses in preparation for the PhD qualifying examination. There are four area of concentration in department:
Course Description
MCDMBayesian decision models; decision trees; value of information; utility theory, use of judgmental probability, study of strategies; economics of sampling; risk sharing and decisions; implementation of decision models. Back to TopFuzzy logictopic of intelligent control by using genetic algorithms to learn fuzzy rules in fuzzy logic control systems. application in mathematical programming,Decision making, fuzzy ranking problems .Application to the control systems are also briefly discussed. Back to TopAPOFundamentals of supply chain management and enterprise resources planning (ERP); aggregate production planning: static, dynamic, nonlinear and lot sizing models; operations scheduling: flow shops and job shops; materials management and materials requirement planning (MRP); capacity resources planning (CRP); distribution system management; implementation of manufacturing management strategies. Back to TopAPPIntroduction to a variety of production and inventory control planning problems; the development of mathematical models corresponding to these problems; a study of approaches for finding solutions. Back to TopDOEIntroduction to the analysis of data from planned experiments. Analysis of variance and regression analysis with applications in engineering. Back to TopNPSOrder 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. Back to TopGTDirected and undirected graphs. Bipartite graphs. Hamilton cycles and Euler tours. Connectedness, matching, and coloring. Flows in capacity-constrained networks. Maximum flow and minimum cost flow problems. Back to TopNLPNecessary and sufficient conditions for unconstrained and constrained optima. Duality theory. Computational methods for unconstrained (e.g., quasi-Newton) problems, linearly constrained (e.g., active set) problems, and nonlinearly constrained (e.g., successive quadratic programming) problems. Back to TopGDMQuantitative approaches, using decision models. Topics include elements of a decision, theory of optimal decisions, resource allocation, simulated decision making, decisions under uncertainty, risk and pressure, utility theory, and game theory. Back to TopNTIntroduction to graph theory; shortest path and related algorithms; network
flow algorithms; matching and covering algorithms; travelling salesman problem
and extensions; Chinese postman problem and extensions; problems of location on
a network; stochastic network. AITImplementation of information design concepts; management information systems; verification; auditing; checking and controlling information lost in the system; applications; case studies. Back to TopSTIESpecial Topics in Industrial Engineering Back to TopTQMTotal quality management, quality assurance programs, quality circles, modelling process quality, statistical process control, acceptance sampling plans, quality information systems, organization for quality, quality cost models, quality design, recent issues in quality management. Back to TopRTAnalysis of deterministic, probabilistic and stochastic reliability models; coherent structures, min-path and min-cut representations, computing system reliability, reliability importance of components, systems with associated component, bounds on system reliability, stock and wear models, reliability operations and classes of life distributions, reliability improvement and allocation, availability theory for multi-component systems, optimal management of systems by replacement and preventive maintenance. Back to Top[] Back to Topauthor: Jamshid Nazemi
|
|
Azad Science & Technology University, School Of Engineering, |
