Course12710438162 lectures
Multi-Criteria Decision Making and Applications
Prof. Raghu Nandan Sengupta · IIT Kanpur
Lectures
week-01
- Lecture 1 - Example 01, 02, 03
- Lecture 2 - Example 03, 04, 05, 06
- Lecture 3 - Example 04, 05, 06 (Continued...)
- Lecture 4 - Example 06 (Continued...), Example 07
- Lecture 5 - Example 06 (Continued...), Example 07
week-02
- Lecture 6 - MCDM: Definitions, Theory of Choice, MCDM Axioma, Condorcet Paradox
- Lecture 7 - Theory of Choice, MCDM Axioms, Condorcet Paradox
- Lecture 8 - MCDM Axioms, Condorcet Paradox, Utility Theory
- Lecture 9 - Utility Theory, Expected value of Utility Function, Lotteries, Rational Choice, Properties of Utility Function
- Lecture 10 - Rational Choice, Properties of Utility Function, Risk Aversion, Neutrality, Seeking Properties, Marginal Utility, Absolute Risk Aversion, Relative Risk Aversion
week-03
- Lecture 11 - Utility Theory Examples, Properties of Utility Function, Risk Aversion, Neutrality, Seekingi Properties, Marginal Utility, Absolute Risk Aversion, Relative Risk Aversion
- Lecture 12 - Properties of Utility Function, Risk Aversion, Neutrality, Seeking Properties, Marginal Utility, Absolute Risk Aversion, Relative Risk Aversion, Example of Utility Functions
- Lecture 13 - Example of Utility Functions, Certainty Equivalent, Geomatric Mean Methods, Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 14 - Example of Utility Functions, Certainty Equivalent, Geomatric Mean Methods, Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 15 - Certainty Equivalent, Geomatric Mean Methods, Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
week-04
- Lecture 16 - Geomatric Mean Methods, Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 17 - Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 18 - Safety First Principle, Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 19 - Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
- Lecture 20 - Stochastic Dominance, Hyperbolic Absolute Risk Aversion Function
week-05
- Lecture 21 - Concept of Pareto Optimally in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 22 - Concept of Pareto Optimality in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 23 - Pareto Optimality, Property of Dominance, Strong Pareto Optimality, Weak Pareto Optimality, Concepts of Pareto Optimality in 2D Space, Effective versus Inefficient Solutions
- Lecture 24 - Concept of Pareto Optimality in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 25 - Concept of Pareto Optimality in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
week-06
- Lecture 26 - Concept of Pareto Optimality in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 27 - Concept of Pareto Optimality in 2D Space, Effective Versus Inefficient Solutions, Karusch Kuhn Tucker Condition, Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 28 - Scales of Measurements: Nominal Scale, Ordinal Scale, Interval Scale, Ratio Scale, Goal Programming
- Lecture 29 - Pareto Optimality, Concept of Pareto Optimality, Goal Programming
- Lecture 30 - Pareto Optimality, Pareto Curves, Goal Programming
week-07
- Lecture 31 - Goal Programming, Concepts of Pareto Optimality in 2D Space for LP, Concepts of Pareto Optimality in 3D Space for QP
- Lecture 32 - Concepts of Pareto Optimality in 2D Space for LP, Concepts of Pareto Optimality in 3D Space for QP
- Lecture 33 - LP Example of Goal Programming/MCDM, NLP Example of Goal Programming/MCDM
- Lecture 34 - LP Example of Goal Programming/MCDM, NLP Example of Goal Programming/MCDM (Continued...)
- Lecture 35 - LP Example of Goal Programming/MCDM, NLP Example of Goal Programming/MCDM (Continued...)
week-08
- Lecture 36 - LP Example of Goal Programming/MCDM, NLP Example of Goal Programming/MCDM (Continued...)
- Lecture 37 - Quadratic Programming, Goal Programming
- Lecture 38 - Multi Attribute Utility Theory, TOPSIS
- Lecture 39 - Multi Attribute Utility Theory, TOPSIS
- Lecture 40 - Technique For Order Preference by Similarity to Ideal Solutions (TOPSIS)
week-09
- Lecture 41 - Technique For Order Preference by Similarity to Ideal Solutions (TOPSIS)
- Lecture 42 - Technique For Order Preference by Similarity to Ideal Solutions (TOPSIS)
- Lecture 43 - Technique For Order Preference by Similarity to Ideal Solutions (TOPSIS), Elimination and Choice Translating Reality (ELECTRE)
- Lecture 44 - Elimination and Choice Translating Reality (ELECTRE)
- Lecture 45 - Elimination and Choice Translating Reality (ELECTRE)
week-10
- Lecture 46 - Elimination and Choice Translating Reality (ELECTRE)
- Lecture 47 - Elimination and Choice Translating Reality (ELECTRE)
- Lecture 48 - Elimination and Choice Translating Reality (ELECTRE), e-ELECTRE
- Lecture 49 - Elimination and Choice Translating Reality (ELECTRE), e-ELECTRE
- Lecture 50 - Elimination and Choice Translating Reality (ELECTRE), e-ELECTRE, VIKOR
week-11
- Lecture 51 - VIKOR (VIsekriterijumska Optimizacija I Kompromisno Resenje)
- Lecture 52 - VIKOR (VIsekriterijumska Optimizacija I Kompromisno Resenje)
- Lecture 53 - Analytical Hierarchy Process (AHP)
- Lecture 54 - Analytical Hierarchy Process (AHP) (Continued...)
- Lecture 55 - Analytical Hierarchy Process (AHP) (Continued...)
week-12
- Lecture 56 - Data Envelopment Analysis (DEA)
- Lecture 57 - Data Envelopment Analysis (DEA)
- Lecture 58 - Decision Tree Analysis
- Lecture 59 - Decision Tree Analysis (Continued...)
- Lecture 60 - Example in Multi Objective Decision Making
Live Session
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kanpur
- Department
- Multidisciplinary
- Teacher
- Prof. Raghu Nandan Sengupta
- Level
- Undergraduate
- Language
- English
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed