Multi-Criteria Decision Making (MCDM) Using Matlab and Excel

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Operation Research Group

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1. Background of MCDMs

1. Background of MCDMs1.mp4

21:04

2. Simple Additive Weightage (SAW)

1. An Introduction to SAW.mp4

04:49

2. Example 1.mp4

09:03

3. Example 2.mp4

08:43

4. Example 3.mp4

10:54

3. Analytic Hierarchy Process (AHP)

1. An Introduction to AHP.mp4

09:58

2. Example 1.mp4

10:38

3. A framework for AHP.mp4

14:45

4. Coding AHP.mp4

10:49

5. Example 02.mp4

08:36

6. Example 03.mp4

16:02

4. Analytic Network Process (ANP)

1. An Introduction to ANP.mp4

14:29

2. Using Supermatrix in AHP.mp4

05:56

3. Using Supermatrix in AHP-Example02.mp4

06:18

4. ANP-Example01.mp4

06:44

5. ANP-Example02.mp4

06:48

5. Technique for Order Preference and Similarity to Ideal Solution (TOPSIS)

1. An Introduction to TOPSIS.mp4

06:05

2. Implementation of TOPSIS in Excel.mp4

09:34

3. Implementation of TOPSIS in Matlab.mp4

16:27

6. Elimination Et Choice Translating Reality (ELECTRE)

1. An Intropduction to ELECTRE.mp4

09:03

2. Implementation of ELECTRE in Excel.mp4

35:08

3. Implementation of ELECTRE in Matlab.mp4

31:40

7. Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)

1. An Intropduction to PROMETHEE.mp4

06:51

2. Implementation of PROMETHEE in Excel.mp4

15:23

3. Implementation of PROMETHEE in Matlab.mp4

21:25

8. VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)

1. An Intropduction to VIKOR.mp4

06:20

2. Implementation of VIKOR in Excel.mp4

15:34

3. Implementation of VIKOR in Matlab.mp4

13:33

9. Decision-Making Trial and Evaluation Laboratory (DEMATEL)

1. An Intropduction to DEMATEL.mp4

06:19

2. Implementation of DEMATEL in Excel.mp4

14:47

3. Implementation of DEMATEL in Matlab.mp4

09:08

10. Grey Relational Analysis (GRA)

1. An Intropduction to GRA.mp4

06:49

2. Implementation of GRA in Excel.mp4

12:00

3. Implementation of GRA in Matlab.mp4

13:02

11. Multi-objective Optimization on the Basis of Ratio Analysis Method (MOORA)

1. Introduction to MOORA.mp4

04:49

2. Implementation of MOORA in Excel.mp4

08:16

3. Implementation of MOORA in Matlab.mp4

15:07

12. Complex Proportion Assessment Method (COPRAS)

1. Introduction to COPRAS.mp4

05:26

2. Implementation of COPRAS in Excel.mp4

08:22

3. Implementation of COPRAS in Matlab.mp4

11:43

13. Additive Ratio Assessment Method (ARM-ARAS)

1. Introduction to ARAS.mp4

06:30

2. Implementiation of ARAS in Excel.mp4

06:29

3. Implementiation of ARAS in Matlab.mp4

11:30

14. Weighted Aggregated Sum Product Assessment (WASPAS)

1. Introduction to WASPAS.mp4

05:29

2. Implementation of WASPAS in Excel.mp4

10:05

3. Implementation of WASPAS in Matlab.mp4

24:42

15. Stepwise Weight Assessment Ratio Analysis (SWARA)

1. Introduction to SWARA.mp4

04:17

2. Implementation of SWARA in Excel.mp4

04:32

3. Implementation of SWARA in Matlab.mp4

06:38

16. COmbinative Distance-based ASsessment (CODAS)

1. Introduction to CODAS.mp4

04:41

2. Implementation of CODAS in Excel.mp4

13:44

3. Implementation of CODAS in Matlab.mp4

14:17

17. Evaluation Based on Distance from Average Solution (EDAS)

1. Introduction to EDAS.mp4

04:44

2. Implementation of EDAS in Excel.mp4

10:53

3. Implementation of EDAS in Matlab.mp4

14:27

18. Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS

1. Introduction to MARCOS.mp4

04:21

2. Implementation of MARCOS in Excel.mp4

12:07

3. Implementation of MARCOS in Matlab.mp4

16:10

19. CRITIC

1. Introduction to CRITIC.mp4

04:03

2. Implementation of CRITIC in Excel.mp4

11:33

3. Implementation of CRITIC in Matlab.mp4

09:37

20. Entropy

1. Introduction to Entropy.mp4

02:59

2. Implementation of Entropy in Excel.mp4

05:56

3. Implementation of Entropy in Matlab.mp4

04:38

21. Combined Compromise Solution (CoCoSo)

1. Introduction to CoCoSo.mp4

05:06

2. Implementation of CoCoSo in Excel.mp4

11:39

3. Implementation of CoCoSo in Matlab.mp4

13:03

22. FuzzyAHP-Chang

1. An Introduction to Chang method.mp4

07:44

2. Implementing Chang method in Excel.mp4

14:33

3. Implementing Chang method in Matlab.mp4

16:11

4. Introduction to Fuzzy Integral Value.mp4

10:19

5. Implementing Fuzzy Integral Value in Excel.mp4

07:16

6. Implementing Fuzzy Integral Value in Matlab.mp4

12:29

Description

Learn how to implement different approaches using Microsoft Excel and Matlab language programming to solve real-world M

What You'll Learn?

Gain a foundational understanding of the Multi-Criteria Decision Making process and its significance in various fields
Dive deep into the most recent methods and techniques in MCDM, ensuring that students stay at the cutting edge of the discipline.
Acquire proficiency in leveraging Excel to tackle MCDM problems, from basic to advanced levels.
Learn to use Matlab as a powerful tool for addressing MCDM scenarios, from setting up decision problems to solving complex cases.
Equip yourself with the ability to program and customize various MCDM techniques, allowing for flexibility and adaptability in problem-solving

Who is this for?

Those studying operations research, management science, engineering, business analytics, or any field where decision-making processes are crucial.

Engineers, data analysts, operations managers, and others who need to make informed decisions by considering multiple criteria.

Professionals who advise businesses on decision-making strategies and need tools to analyze multiple decision factors simultaneously.

Professors and educators who wish to incorporate MCDM methodologies into their curriculum or research.

Individuals keen on learning how to implement MCDM techniques using popular software tools like MATLAB and Excel.

Executives, managers, or anyone tasked with making complex decisions where multiple factors need to be weighed and considered.

Those with a curious mind about structured decision-making processes and how software can aid in such tasks.

What You Need to Know?

Basic understanding of decision-making processes and principles.

Familiarity with the concept of MCDM and its applications.

Basic proficiency in Microsoft Excel, including functions and data organization.

Introductory knowledge of MATLAB, including its interface and basic commands.

Elementary understanding of linear algebra, matrices, and mathematical optimization.

Basic knowledge of statistics, especially in the context of data analysis.

More details

Description
Multi-Criteria Decision-Making (MCDM) has evolved as a pivotal aspect of operations research, focusing on the creation of computational and mathematical instruments to aid decision-makers in their subjective evaluation of performance criteria.

For the first time on Udemy, we present an all-encompassing course that delves deep into a variety of MCDM methodologies. This practical course is tailored to meet the needs of students, researchers, and professionals in the field.

Our approach to each MCDM methodology is threefold:

We kick-start with an introduction to the foundational theory of the method.
Following this, we provide hands-on implementation using Microsoft Excel.
To conclude, we transition into coding the method with Matlab programming.
Course Outline:

Introduction to MCDM
Simple Additive Weightage (SAW)
Analytic Hierarchy Process (AHP)
Analytic Network Process (ANP)
Technique for Order Preference and Similarity to Ideal Solution (TOPSIS)
Elimination Et Choice Translating Reality (ELECTRE)
Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)
VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)
Decision-Making Trial and Evaluation Laboratory (DEMATEL)
Grey Relational Analysis (GRA)
Multi-objective Optimization on the Basis of Ratio Analysis Method (MOORA)
Complex Proportion Assessment Method (COPRAS)
Additive Ratio Assessment Method (ARM-ARAS)
Weighted Aggregated Sum Product Assessment (WASPAS)
Stepwise Weight Assessment Ratio Analysis (SWARA)
COmbinative Distance-based ASsessment (CODAS)
Evaluation Based on Distance from Average Solution (EDAS)
Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS)
CRiteria Importance Through Intercriteria Correlation (CRITIC)
Entropy Weighting Technique
Additionally, the course is enriched with an extensive selection of coding tutorials, ensuring students have ample opportunities to cement their theoretical understanding with practical exercises.

Upon successful completion of this course, participants will be well-equipped to apply Excel and Matlab to a plethora of MCDM challenges. This foundation will also pave the way for exploring and mastering other MCDM strategies.
Who this course is for:
Those studying operations research, management science, engineering, business analytics, or any field where decision-making processes are crucial.
Engineers, data analysts, operations managers, and others who need to make informed decisions by considering multiple criteria.
Professionals who advise businesses on decision-making strategies and need tools to analyze multiple decision factors simultaneously.
Professors and educators who wish to incorporate MCDM methodologies into their curriculum or research.
Individuals keen on learning how to implement MCDM techniques using popular software tools like MATLAB and Excel.
Executives, managers, or anyone tasked with making complex decisions where multiple factors need to be weighed and considered.
Those with a curious mind about structured decision-making processes and how software can aid in such tasks.

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We are a dedicated group of professionals who have been working in the field of Operations Research for more than eight years. Our team is comprised of individuals with diverse backgrounds and expertise, which enables us to approach problems from multiple angles and provide comprehensive solutions.Our journey began eight years ago when we came together with a shared passion for using analytical methods to solve complex problems. Since then, we have dedicated ourselves to mastering the various tools and techniques of Operations Research, such as linear programming, simulation, and optimization, among others.Over the years, we have worked on a wide range of areas within Operations Research, including but not limited to supply chain management, transportation, logistics, inventory control, and production scheduling. Our extensive experience across various sectors has equipped us with the knowledge and skills to tackle a myriad of challenges faced by businesses today.We pride ourselves on our ability to develop innovative solutions that not only address the immediate needs of our clients but also lay the foundation for sustainable growth and success in the future. Our approach is collaborative, and we work closely with our clients to understand their unique needs and tailor our solutions accordingly.Our commitment to excellence and continuous improvement has earned us a reputation as trusted advisors in the field of Operations Research. We are dedicated to helping our clients achieve their objectives and realize their full potential.

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