{"id":229,"date":"2016-07-21T06:44:40","date_gmt":"2016-07-21T06:44:40","guid":{"rendered":"http:\/\/www.inspirenignite.com\/jntuk\/?p=229"},"modified":"2016-08-07T12:08:46","modified_gmt":"2016-08-07T12:08:46","slug":"jntuk-b-tech-optimization-techniques-open-elective-for-r13-batch","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuk\/jntuk-b-tech-optimization-techniques-open-elective-for-r13-batch\/","title":{"rendered":"JNTUK B.Tech Optimization Techniques (Open Elective) for R13 Batch."},"content":{"rendered":"<p>JNTUK B.Tech Optimization Techniques (Open Elective) R13 Syllabus for Engineering it gives you detail information about Optimization Techniques (Open Elective) syllabus.<\/p><div class=\"a9916ad81d5189659b0bfae0b37c143c\" data-index=\"1\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block; text-align:center;\"\r\n     data-ad-layout=\"in-article\"\r\n     data-ad-format=\"fluid\"\r\n     data-ad-client=\"ca-pub-1181153414625576\"\r\n     data-ad-slot=\"9648548092\"><\/ins>\r\n<script>\r\n     (adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script>\n<\/div>\n\n<p><strong>Preamble<\/strong><\/p>\n<p>Optimization techniques have gained importance to solve many engineering design problems by developing linear and nonlinear mathematical models. The aim of this course is to educate the student to develop a mathematical model by defining an objective function and constraints in terms of design variables and then apply a particular mathematical programming technique. This course covers classical optimization techniques, linear programming, nonlinear programming and dynamic programming techniques.<\/p>\n<p><strong>Learning Objectives<\/strong><\/p>\n<ul>\n<li>To define an objective function and constraint functions in terms of design variables, and then state the optimization problem.<\/li>\n<li>To state single variable and multi variable optimization problems, without and with constraints.<\/li>\n<li>To explain linear programming technique to an optimization problem, define slack and surplus variables, by using Simplex method.<\/li>\n<li>To state transportation and assignment problem as a linear programming problem to determine optimality conditions by using Simplex method.<\/li>\n<li>To study and explain nonlinear programming techniques, unconstrained or constrained, and define exterior and interior penalty functions for optimization problems.<\/li>\n<li>To explain Dynamic programming technique as a powerful tool for making a sequence of interrelated decisions.<\/li>\n<\/ul>\n<p><strong>UNIT \u2013 I<\/strong><\/p>\n<p><strong>Introduction and Classical Optimization Techniques:<\/strong> Statement of an Optimization problem \u2013 design vector \u2013 design constraints \u2013 constraint surface \u2013 objective function \u2013 objective function surfaces \u2013 classification of Optimization problems.<\/p>\n<p><strong>UNIT \u2013 II<\/strong><\/p>\n<p><strong>Classical Optimization Techniques :<\/strong> Single variable Optimization \u2013 multi variable Optimization without constraints \u2013 necessary and sufficient conditions for minimum\/maximum \u2013 multivariable Optimization with equality constraints. Solution by method of Lagrange multipliers \u2013 multivariable Optimization with inequality constraints \u2013 Kuhn \u2013 Tucker conditions.<\/p>\n<p><strong>UNIT \u2013 III<\/strong><\/p>\n<p><strong>Linear Programming :<\/strong> Standard form of a linear programming problem \u2013 geometry of linear programming problems \u2013 definitions and theorems \u2013 solution of a system of linear simultaneous equations \u2013 pivotal reduction of a general system of equations \u2013 motivation to the simplex method \u2013 simplex algorithm &#8211; Duality in Linear Programming \u2013 Dual Simplex method.<\/p>\n<p><strong>UNIT \u2013 IV<\/strong><\/p>\n<p><strong>Transportation Problem :<\/strong> Finding initial basic feasible solution by north \u2013 west corner rule, least cost method and Vogel\u2019s approximation method \u2013 testing for optimality of balanced transportation problems \u2013 Special cases in transportation problem.<\/p>\n<p><strong>UNIT \u2013 V<\/strong><\/p>\n<p><strong>Nonlinear Programming :<\/strong> Unconstrained cases &#8211; One \u2013 dimensional minimization methods: Classification, Fibonacci method and Quadratic interpolation method &#8211; Univariate method, Powell\u2019s method and steepest descent method. Constrained cases &#8211; Characteristics of a constrained problem, Classification, Basic approach of Penalty Function method; Basic approaches of Interior and\u00a0Exterior penalty function methods. Introduction to convex Programming Problem.<\/p>\n<p><strong>UNIT \u2013 VI<\/strong><\/p>\n<p><strong>Dynamic Programming<\/strong> : Dynamic programming multistage decision processes \u2013 types \u2013 concept of\u00a0sub optimization and the principle of optimality \u2013computational procedure in dynamic programming \u2013 examples illustrating the calculus method of solution &#8211; examples illustrating the tabular method of solution.<\/p>\n<p><strong>Learning Outcomes<\/strong><\/p>\n<p>The student should be able to:<\/p>\n<ul>\n<li>State and formulate the optimization problem, without and with constraints, by using design variables from an engineering design problem.<\/li>\n<li>Apply classical optimization techniques to minimize or maximize a multi-variable objective function, without or with constraints, and arrive at an optimal solution.<\/li>\n<li>Formulate a mathematical model and apply linear programming technique by using Simplex method. Also extend the concept of dual Simplex method for optimal solutions.<\/li>\n<li>Solve transportation and assignment problem by using Linear programming Simplex method. Apply gradient and non-gradient methods to nonlinear optimization<\/li>\n<li>problems and use interior or exterior penalty functions for the constraints to derive the optimal solutions.<\/li>\n<li>Formulate and apply Dynamic programming technique to inventory control, production planning, engineering design problems etc. to reach a final optimal solution from the current optimal solution.<\/li>\n<\/ul>\n<p><strong>Text Books<\/strong><\/p>\n<ul>\n<li>\u201cEngineering optimization: Theory and practice\u201d-by S. S.Rao, New Age International (P) Limited, 3rd edition, 1998.<\/li>\n<li>\u201cIntroductory Operations Research\u201d by H.S. Kasene &amp; K.D. Kumar, Springer (India), Pvt. LTd.<\/li>\n<\/ul>\n<p><strong>Reference Books<\/strong><\/p>\n<ul>\n<li>\u201cOptimization Methods in Operations Research and systems Analysis\u201d \u2013 by K.V. Mital and C. Mohan, New Age International (P)<\/li>\n<li>Limited, Publishers, 3rd edition, 1996.<\/li>\n<li>Operations Research \u2013 by Dr. S.D.Sharma, Kedarnath, Ramnath &amp; Co<\/li>\n<li>\u201cOperations Research: An Introduction\u201d \u2013 by H.A.Taha, PHI Pvt. Ltd., 6th edition<\/li>\n<li>Linear Programming\u2013by G.Hadley.<\/li>\n<\/ul>\n<p><strong>Note<\/strong> : This Elective can be offered to Students of All Branches except EEE.<\/p>\n<p>For more information about all JNTU updates please stay connected to us on FB and don\u2019t hesitate to ask any questions in the comment.<\/p>\n<div class=\"a9916ad81d5189659b0bfae0b37c143c\" data-index=\"2\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block; text-align:center;\"\r\n     data-ad-layout=\"in-article\"\r\n     data-ad-format=\"fluid\"\r\n     data-ad-client=\"ca-pub-1181153414625576\"\r\n     data-ad-slot=\"8060844699\"><\/ins>\r\n<script>\r\n     (adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script>\n<\/div>\n\n<div style=\"font-size: 0px; height: 0px; line-height: 0px; margin: 0; padding: 0; clear: both;\"><\/div>","protected":false},"excerpt":{"rendered":"<p>JNTUK B.Tech Optimization Techniques (Open Elective) R13 Syllabus for Engineering it gives you detail information about Optimization Techniques (Open Elective) syllabus. Preamble Optimization techniques have gained importance to solve many [&hellip;]<\/p>\n","protected":false},"author":2259,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-229","post","type-post","status-publish","format-standard","hentry","category-syllabus"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/posts\/229","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/users\/2259"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/comments?post=229"}],"version-history":[{"count":2,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/posts\/229\/revisions"}],"predecessor-version":[{"id":410,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/posts\/229\/revisions\/410"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/media?parent=229"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/categories?post=229"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/jntuk\/wp-json\/wp\/v2\/tags?post=229"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}