{"id":12555,"date":"2018-09-12T16:09:46","date_gmt":"2018-09-12T16:09:46","guid":{"rendered":"https:\/\/www.inspirenignite.com\/jntuh\/?p=12555"},"modified":"2026-05-24T10:27:27","modified_gmt":"2026-05-24T04:57:27","slug":"jntuh-m-tech-2017-2018-r17-detailed-syllabus-optimization-techniques-and-applications","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuh\/jntuh-m-tech-2017-2018-r17-detailed-syllabus-optimization-techniques-and-applications\/","title":{"rendered":"JNTUH M.Tech 2017-2018 (R17) Detailed Syllabus Optimization Techniques and Applications"},"content":{"rendered":"Optimization Techniques and Applications Detailed Syllabus for Design For Manufacturing\/ Design and Manufacturing M.Tech first year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.\r\n\r\nThe detailed syllabus for Optimization Techniques and Applications M.Tech 2017-2018 (R17) first year second sem is as follows.\r\n\r\nM.Tech. I Year II Sem.\r\n\r\nUNIT- I : Single Variable Non-Linear Unconstrained Optimization:<\/strong> One dimensional Optimization methods:- Uni-modal function, elimination methods, ,, Fibonacci method, golden section method, interpolation methods \u2013 quadratic & cubic interpolation methods.\r\n\r\nUNIT- II : Multi variable non-linear unconstrained optimization:<\/strong> Direct search method \u2013 Univariant method – pattern search methods \u2013 Powell\u2019s- Hook -Jeeves, Rosenbrock search methods- gradient methods, gradient of function, steepest decent method, Fletcher Reeves method, variable metric method.\r\n\r\nUNIT- III : Linear Programming:<\/strong> Formulation \u2013 Sensitivity analysis. Change in the constraints, cost coefficients, coefficients of the constraints, addition and deletion of variable, constraints. Simulation \u2013 Introduction \u2013 Types- steps \u2013 application \u2013 inventory \u2013 queuing \u2013 thermal system\r\n\r\nUNIT – IV : Integer Programming:<\/strong> Introduction \u2013 formulation \u2013 Gomory cutting plane algorithm \u2013 Zero or one algorithm, branch and bound method Stochastic programming: Basic concepts of probability theory, random variables- distributions-mean, variance, correlation, co variance, joint probability distribution- stochastic linear, dynamic programming.\r\n\r\nUNIT – V : Geometric Programming:<\/strong> Polynomials \u2013 arithmetic – geometric inequality \u2013 unconstrained G.Pconstrained G.P (<= TYPE ONLY) Non-traditional optimization Techniques: Genetic Algorithms-Steps-Solving simple problemsComparitions of similarities and dissimilarities between traditional and non-traditional techniques-Particle Swarm Optimization (PSO)- Steps(Just understanding)-Simulated Annealing-Steps-Simple problems.\r\n\r\nREFERENCES:<\/strong>\r\n