This document outlines the syllabus and evaluation criteria for the Optimization Techniques course taught by Dr. Mantra Prasad Satpathy. It discusses that students will be evaluated based on an end semester exam, mid semester exam, assignments, class tests, and attendance. The syllabus covers topics like linear programming, sensitivity analysis, statistics, neural networks, fuzzy logic, and genetic algorithms. Communication for the class will be through hard copy class notes, a Google Classroom, and WhatsApp group.

1. Optimization Techniques
(ME6116)
Dr. Mantra Prasad Satpathy
School of Mechanical Engineering
KIIT (Deemed-to-be University), Bhubaneswar
E-mail: mantra.satpathyfme@kiit.ac.in
Lecture 1

2. Communication
1. Hard copy
• Separate class note
• Start from a fresh page with Class Serial No. &
Date
• Must bring it to every class
2. Soft copy
• Google Classroom
• Whats app: Optimization-2022-ME6116-KIITBBSR
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3. Evaluation
1. End Sem : 50
2. Mid Sem : 20
3. Teacher Assessment : 30
a) Assignments : 10
b) Class tests : 10
c) Attendance, Punctuality, Discipline etc. : 10
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4. Evaluation
3. Class Test : 10
• Written Test: Quiz
• Oral Test:
i) Questions in every class
ii) Simple questions from what taught in previous few
classes
iii) At least rear class note (if absent in any class
copy down the lecture from friend’s class note)
before coming to class
iv) Come prepared as if every class is an exam
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5. Syllabus
1. Introduction to optimization: Design vector, design
constraints, constraint surface, objective function, classification of
optimization problems.
2. Single variable optimization problem: Assignment
problem, Travelling Salesman problem, Transportation problem,
Problem of Degeneracy, Game Theory, Dominance Principle
3. Linear Programming Problem (LPP): Mathematical
Formulations of the problem, general linear programming problem,
canonical and standard forms Fundamental properties of solution to
L.P.P. Computational procedure. Simplex method, Artificial variable
techniques, Big-M & Two phase method, Problem of Degeneracy,
Concept of Dualities, IPP, NLPP
4. Sensitivities Analysis: Discrete changes in cost vector and
requirement vector.
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6. Syllabus
5. Statistics & design of experiments: Frequency
Distribution & Histograms, Probability & its Distribution, Measures of
Central Tendency & Distribution, Presentation of Statistical Data.
Confidence intervals, Hypothesis Testing, Correlation, Liner &
Multiple Repression Analysis, Signification Testing. Full & fractional
factorial experiments, analysis of variance, Latin squares, response
surface methodology, Taguchi techniques.
6. Neural Networks: Machine Learning Using Neural Network -
Adaptive Networks – Feed forward Networks – Supervised Learning
Neural Networks – Radial Basis Function Networks – Reinforcement
Learning – Unsupervised Learning Neural Networks – Adaptive
Resonance architectures – Advances in Neural networks.
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7. Syllabus
7. Fuzzy Logic: Fuzzy Sets – Operations on Fuzzy Sets – Fuzzy
Relations – Membership Functions- Fuzzy Rules and Fuzzy
Reasoning – Fuzzy Inference Systems – Fuzzy Expert Systems –
Fuzzy Decision Making. Neuro-Fuzzy Modelling: Adaptive Neuro-
Fuzzy Inference Systems – Coactive Neuro-Fuzzy Modelling –
Classification and Regression Trees – Data Clustering Algorithms –
Rule base Structure Identification – Neuro-Fuzzy Control – Case
studies.
8. Genetic Algorithms: Introduction to Genetic Algorithms –
Applications of GA in Machine Learning - Machine Learning Approach
to Knowledge Acquisition – Reproduction – Crossover – Mutation.
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8. Text Books: Essential Reading
1. Engineering Optimization: Theory and Practice, S. S. Rao, New Age
International (P) Ltd, 3rd Edition
2. Soft Computing, D.K. Pratihar, Narosa Publications
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9. Reference Books: Supplementary Reading
1. Design & Analysis of Experiments, M.C. Montgomery, John Wiley & Sons,
2006
2. Quality & Robust Engineering, M.S. Phadke, Prentice Hall,1989, 1st
edition
3. Taguchi Techniques in Quality Engineering, P. J. Ross, McGraw-Hill
Professional, 1995, 2nd editions
4. Engineering Optimization, Ravindran and Phillips, McGraw Hill.
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