Multi-Criteria Decision Making (Mcdm) Using Matlab And Excel

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

Requirements

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.

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 MCDMSimple 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 TechniqueAdditionally, 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.

Overview

Section 1: Background of MCDMs

Lecture 1 Background of MCDMs1

Section 2: Simple Additive Weightage (SAW)

Lecture 2 An Introduction to SAW

Lecture 3 Example 1

Lecture 4 Example 2

Lecture 5 Example 3

Section 3: Analytic Hierarchy Process (AHP)

Lecture 6 An Introduction to AHP

Lecture 7 Example 1

Lecture 8 A framework for AHP

Lecture 9 Coding AHP

Lecture 10 Example 02

Lecture 11 Example 03

Section 4: Analytic Network Process (ANP)

Lecture 12 An Introduction to ANP

Lecture 13 Using Supermatrix in AHP

Lecture 14 Using Supermatrix in AHP-Example02

Lecture 15 ANP-Example01

Lecture 16 ANP-Example02

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

Lecture 17 An Introduction to TOPSIS

Lecture 18 Implementation of TOPSIS in Excel

Lecture 19 Implementation of TOPSIS in Matlab

Section 6: Elimination Et Choice Translating Reality (ELECTRE)

Lecture 20 An Intropduction to ELECTRE

Lecture 21 Implementation of ELECTRE in Excel

Lecture 22 Implementation of ELECTRE in Matlab

Section 7: Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)

Lecture 23 An Intropduction to PROMETHEE

Lecture 24 Implementation of PROMETHEE in Excel

Lecture 25 Implementation of PROMETHEE in Matlab

Section 8: VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)

Lecture 26 An Intropduction to VIKOR

Lecture 27 Implementation of VIKOR in Excel

Lecture 28 Implementation of VIKOR in Matlab

Section 9: Decision-Making Trial and Evaluation Laboratory (DEMATEL)

Lecture 29 An Intropduction to DEMATEL

Lecture 30 Implementation of DEMATEL in Excel

Lecture 31 Implementation of DEMATEL in Matlab

Section 10: Grey Relational Analysis (GRA)

Lecture 32 An Intropduction to GRA

Lecture 33 Implementation of GRA in Excel

Lecture 34 Implementation of GRA in Matlab

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

Lecture 35 Introduction to MOORA

Lecture 36 Implementation of MOORA in Excel

Lecture 37 Implementation of MOORA in Matlab

Section 12: Complex Proportion Assessment Method (COPRAS)

Lecture 38 Introduction to COPRAS

Lecture 39 Implementation of COPRAS in Excel

Lecture 40 Implementation of COPRAS in Matlab

Section 13: Additive Ratio Assessment Method (ARM-ARAS)

Lecture 41 Introduction to ARAS

Lecture 42 Implementiation of ARAS in Excel

Lecture 43 Implementiation of ARAS in Matlab

Section 14: Weighted Aggregated Sum Product Assessment (WASPAS)

Lecture 44 Introduction to WASPAS

Lecture 45 Implementation of WASPAS in Excel

Lecture 46 Implementation of WASPAS in Matlab

Section 15: Stepwise Weight Assessment Ratio Analysis (SWARA)

Lecture 47 Introduction to SWARA

Lecture 48 Implementation of SWARA in Excel

Lecture 49 Implementation of SWARA in Matlab

Section 16: COmbinative Distance-based ASsessment (CODAS)

Lecture 50 Introduction to CODAS

Lecture 51 Implementation of CODAS in Excel

Lecture 52 Implementation of CODAS in Matlab

Section 17: Evaluation Based on Distance from Average Solution (EDAS)

Lecture 53 Introduction to EDAS

Lecture 54 Implementation of EDAS in Excel

Lecture 55 Implementation of EDAS in Matlab

Section 18: Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS

Lecture 56 Introduction to MARCOS

Lecture 57 Implementation of MARCOS in Excel

Lecture 58 Implementation of MARCOS in Matlab

Section 19: CRITIC

Lecture 59 Introduction to CRITIC

Lecture 60 Implementation of CRITIC in Excel

Lecture 61 Implementation of CRITIC in Matlab

Section 20: Entropy

Lecture 62 Introduction to Entropy

Lecture 63 Implementation of Entropy in Excel

Lecture 64 Implementation of Entropy in Matlab

Section 21: Combined Compromise Solution (CoCoSo)

Lecture 65 Introduction to CoCoSo

Lecture 66 Implementation of CoCoSo in Excel

Lecture 67 Implementation of CoCoSo in Matlab

Section 22: FuzzyAHP-Chang

Lecture 68 An Introduction to Chang method

Lecture 69 Implementing Chang method in Excel

Lecture 70 Implementing Chang method in Matlab

Lecture 71 Introduction to Fuzzy Integral Value

Lecture 72 Implementing Fuzzy Integral Value in Excel

Lecture 73 Implementing Fuzzy Integral Value in Matlab

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.