CCS357: Optimization Techniques syllabus for AI&ML 2021 regulation (Professional Elective-VII)

Optimization Techniques detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Machine Learning 6th Sem scheme and its subjects, do visit AI&ML 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&ML Professional Elective-VII syllabus scheme. The detailed syllabus of optimization techniques is as follows.

Course Objectives:

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Unit I

LINEAR MODELS
Introduction of Operations Research – mathematical formulation of LPP- Graphical Methods to solve LPP- Simplex Method- Two-Phase method

Unit II

INTEGER PROGRAMMING AND TRANSPORTATION PROBLEMS
Integer programming: Branch and bound method- Transportation and Assignment problems -Traveling salesman problem.

Unit III

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Unit IV

CLASSICAL OPTIMIZATION THEORY
Unconstrained problems – necessary and sufficient conditions – Newton-Raphson method, Constrained problems – equality constraints – inequality constraints – Kuhn-Tucker conditions.

Unit V

QUEUING MODELS
Introduction, Queuing Theory, Operating characteristics of a Queuing system, Constituents of a Queuing system, Service facility, Queue discipline, Single channel models, multiple service channels.

Practicals

1. Solving simplex maximization problems using R programming.
2. Solving simplex minimization problems using R programming.
3. Solving mixed constraints problems – Big M & Two phase method using TORA.
4. Solving transportation problems using R.
5. Solving assignment problems using R.
6. Solving optimization problems using LINGO.
7. Studying Primal-Dual relationships in LP using TORA.
8. Solving LP problems using dual simplex method using TORA.
9. Sensitivity & post optimality analysis using LINGO.
10. Solving shortest route problems using optimization software
11. Solving Project Management problems using optimization software
12. Testing random numbers and random variates for their uniformity.
13. Testing random numbers and random variates for their independence
14. Solve single server queuing model using simulation software package.
15. Solve multi server queuing model using simulation software package.

Course Outcomes:

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Text Books:

1. Hamdy A Taha, Operations Research: An Introduction, Pearson, 10th Edition, 2017.

Reference Books:

1. ND Vohra, Quantitative Techniques in Management, Tata McGraw Hill, 4th Edition, 2011.
2. J. K. Sharma, Operations Research Theory and Applications, Macmillan, 5th Edition, 2012.
3. Hiller F.S, Liberman G.J, Introduction to Operations Research, 10th Edition McGraw Hill, 2017.
4. Jit. S. Chandran, Mahendran P. Kawatra, KiHoKim, Essentials of Linear Programming, Vikas Publishing House Pvt.Ltd. New Delhi, 1994.
5. Ravindran A., Philip D.T., and Solberg J.J., Operations Research, John Wiley, 2nd Edition, 2007.

For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 6th Sem, visit AI&ML 6th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.