Multi-Criteria Decision Making (MCDM) Using Matlab and Excel
Multi-Criteria Decision Making (MCDM) Using Matlab and Excel, available at $19.99, has an average rating of 4.1, with 73 lectures, based on 13 reviews, and has 216 subscribers.
You will learn about 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 This course is ideal for individuals who are Those studying operations research, management science, engineering, business analytics, or any field where decision-making processes are crucial. or Engineers, data analysts, operations managers, and others who need to make informed decisions by considering multiple criteria. or Professionals who advise businesses on decision-making strategies and need tools to analyze multiple decision factors simultaneously. or Professors and educators who wish to incorporate MCDM methodologies into their curriculum or research. or Individuals keen on learning how to implement MCDM techniques using popular software tools like MATLAB and Excel. or Executives, managers, or anyone tasked with making complex decisions where multiple factors need to be weighed and considered. or Those with a curious mind about structured decision-making processes and how software can aid in such tasks. It is particularly useful for Those studying operations research, management science, engineering, business analytics, or any field where decision-making processes are crucial. or Engineers, data analysts, operations managers, and others who need to make informed decisions by considering multiple criteria. or Professionals who advise businesses on decision-making strategies and need tools to analyze multiple decision factors simultaneously. or Professors and educators who wish to incorporate MCDM methodologies into their curriculum or research. or Individuals keen on learning how to implement MCDM techniques using popular software tools like MATLAB and Excel. or Executives, managers, or anyone tasked with making complex decisions where multiple factors need to be weighed and considered. or Those with a curious mind about structured decision-making processes and how software can aid in such tasks.
Enroll now: Multi-Criteria Decision Making (MCDM) Using Matlab and Excel
Summary
Title: Multi-Criteria Decision Making (MCDM) Using Matlab and Excel
Price: $19.99
Average Rating: 4.1
Number of Lectures: 73
Number of Published Lectures: 73
Number of Curriculum Items: 73
Number of Published Curriculum Objects: 73
Original Price: $109.99
Quality Status: approved
Status: Live
What You Will 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 Should Attend
- 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.
Target Audiences
- 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.
Multi-Criteria Decision-Making (MCDM) stands at the forefront of operations research, dedicated to developing sophisticated computational and mathematical tools to assist decision-makers in evaluating performance criteria objectively.
For the first time on Udemy, we’re excited to introduce a comprehensive course that dives deep into the diverse world of MCDM methodologies. Designed specifically for students, researchers, and industry professionals, this practical course aims to bridge the gap between theoretical knowledge and real-world application.
Our unique approach to exploring each MCDM methodology unfolds in three distinct phases:
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Introduction to Theory: We begin with a thorough exploration of the foundational principles underlying each method.
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Hands-on Implementation: Participants will learn to apply these methods using Microsoft Excel, emphasizing practical skills.
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Coding with Matlab: The course culminates with participants coding the methodologies in Matlab, enhancing their technical proficiency.
Course Highlights Include:
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Fundamentals of MCDM
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Simple Additive Weightage (SAW)
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Analytic Hierarchy Process (AHP)
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Analytic Network Process (ANP)
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Technique for Order Preference and Similarity to Ideal Solution (TOPSIS)
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Elimination Et Choice Translating Reality (ELECTRE)
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Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)
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VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)
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Decision-Making Trial and Evaluation Laboratory (DEMATEL)
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Grey Relational Analysis (GRA)
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Multi-objective Optimization on the Basis of Ratio Analysis (MOORA)
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Complex Proportion Assessment (COPRAS)
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Additive Ratio Assessment (ARM-ARAS)
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Weighted Aggregated Sum Product Assessment (WASPAS)
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Stepwise Weight Assessment Ratio Analysis (SWARA)
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COmbinative Distance-based ASsessment (CODAS)
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Evaluation Based on Distance from Average Solution (EDAS)
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Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS)
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CRiteria Importance Through Intercriteria Correlation (CRITIC)
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Entropy Weighting Technique
The course is further enhanced with a wealth of coding tutorials, offering students numerous opportunities to solidify their understanding through practical application.
Upon completing this course, participants will possess the skills to adeptly use Excel and Matlab for addressing various MCDM challenges, laying a solid foundation for mastering additional MCDM techniques.
Course Curriculum
Chapter 1: Background of MCDMs
Lecture 1: Background of MCDMs1
Chapter 2: Simple Additive Weightage (SAW)
Lecture 1: An Introduction to SAW
Lecture 2: Example 1
Lecture 3: Example 2
Lecture 4: Example 3
Chapter 3: Analytic Hierarchy Process (AHP)
Lecture 1: An Introduction to AHP
Lecture 2: Example 1
Lecture 3: A framework for AHP
Lecture 4: Coding AHP
Lecture 5: Example 02
Lecture 6: Example 03
Chapter 4: Analytic Network Process (ANP)
Lecture 1: An Introduction to ANP
Lecture 2: Using Supermatrix in AHP
Lecture 3: Using Supermatrix in AHP-Example02
Lecture 4: ANP-Example01
Lecture 5: ANP-Example02
Chapter 5: Technique for Order Preference and Similarity to Ideal Solution (TOPSIS)
Lecture 1: An Introduction to TOPSIS
Lecture 2: Implementation of TOPSIS in Excel
Lecture 3: Implementation of TOPSIS in Matlab
Chapter 6: Elimination Et Choice Translating Reality (ELECTRE)
Lecture 1: An Intropduction to ELECTRE
Lecture 2: Implementation of ELECTRE in Excel
Lecture 3: Implementation of ELECTRE in Matlab
Chapter 7: Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)
Lecture 1: An Intropduction to PROMETHEE
Lecture 2: Implementation of PROMETHEE in Excel
Lecture 3: Implementation of PROMETHEE in Matlab
Chapter 8: VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)
Lecture 1: An Intropduction to VIKOR
Lecture 2: Implementation of VIKOR in Excel
Lecture 3: Implementation of VIKOR in Matlab
Chapter 9: Decision-Making Trial and Evaluation Laboratory (DEMATEL)
Lecture 1: An Intropduction to DEMATEL
Lecture 2: Implementation of DEMATEL in Excel
Lecture 3: Implementation of DEMATEL in Matlab
Chapter 10: Grey Relational Analysis (GRA)
Lecture 1: An Intropduction to GRA
Lecture 2: Implementation of GRA in Excel
Lecture 3: Implementation of GRA in Matlab
Chapter 11: Multi-objective Optimization on the Basis of Ratio Analysis Method (MOORA)
Lecture 1: Introduction to MOORA
Lecture 2: Implementation of MOORA in Excel
Lecture 3: Implementation of MOORA in Matlab
Chapter 12: Complex Proportion Assessment Method (COPRAS)
Lecture 1: Introduction to COPRAS
Lecture 2: Implementation of COPRAS in Excel
Lecture 3: Implementation of COPRAS in Matlab
Chapter 13: Additive Ratio Assessment Method (ARM-ARAS)
Lecture 1: Introduction to ARAS
Lecture 2: Implementiation of ARAS in Excel
Lecture 3: Implementiation of ARAS in Matlab
Chapter 14: Weighted Aggregated Sum Product Assessment (WASPAS)
Lecture 1: Introduction to WASPAS
Lecture 2: Implementation of WASPAS in Excel
Lecture 3: Implementation of WASPAS in Matlab
Chapter 15: Stepwise Weight Assessment Ratio Analysis (SWARA)
Lecture 1: Introduction to SWARA
Lecture 2: Implementation of SWARA in Excel
Lecture 3: Implementation of SWARA in Matlab
Chapter 16: COmbinative Distance-based ASsessment (CODAS)
Lecture 1: Introduction to CODAS
Lecture 2: Implementation of CODAS in Excel
Lecture 3: Implementation of CODAS in Matlab
Chapter 17: Evaluation Based on Distance from Average Solution (EDAS)
Lecture 1: Introduction to EDAS
Lecture 2: Implementation of EDAS in Excel
Lecture 3: Implementation of EDAS in Matlab
Chapter 18: Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS
Lecture 1: Introduction to MARCOS
Lecture 2: Implementation of MARCOS in Excel
Lecture 3: Implementation of MARCOS in Matlab
Chapter 19: CRITIC
Lecture 1: Introduction to CRITIC
Lecture 2: Implementation of CRITIC in Excel
Lecture 3: Implementation of CRITIC in Matlab
Chapter 20: Entropy
Lecture 1: Introduction to Entropy
Lecture 2: Implementation of Entropy in Excel
Lecture 3: Implementation of Entropy in Matlab
Chapter 21: Combined Compromise Solution (CoCoSo)
Lecture 1: Introduction to CoCoSo
Lecture 2: Implementation of CoCoSo in Excel
Lecture 3: Implementation of CoCoSo in Matlab
Chapter 22: FuzzyAHP-Chang
Lecture 1: An Introduction to Chang method
Lecture 2: Implementing Chang method in Excel
Lecture 3: Implementing Chang method in Matlab
Lecture 4: Introduction to Fuzzy Integral Value
Lecture 5: Implementing Fuzzy Integral Value in Excel
Lecture 6: Implementing Fuzzy Integral Value in Matlab
Instructors
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Operation Research Group
A group of researcher
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 5 votes
- 5 stars: 6 votes
Frequently Asked Questions
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