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Multi-Criteria Decision Making (Mcdm) Using Matlab And Excel
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Multi-Criteria Decision Making (Mcdm) Using Matlab And Excel
Published 9/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.16 GB | Duration: 12h 0m

Learn how to implement different approaches using Microsoft Excel and Matlab programming to solve MCDM problems



What you'll learn
Basic concepts and terms related to Multi-Criteria Decision Making (MCDM)
Basic rules of Matlab programming needed for implementing any MCDMs
Basic Skills of Excel needed for implementing any MCDMs
How to solve any MCDM problem via Matlab and Excel

Requirements
Basic knowledge in Multi-Criteria Decision Making (MCDM) approaches

Description
Multiple Criteria Decision-Making (MCDM) has grown as a part of operations research, concerned with designing computational and mathematical tools for supporting the subjective evaluation of performance criteria by decision-makers.This is the first time that a comprehensive course has been launched on Udemy focusing on a wide range of multi-criteria decision-making (MCDM) approaches. Therefore, we have launched a practical course in the domain of MCDM required for students, researchers and practitioners.All in all, for any given MCDM approach, Firstly, we introduce the basic theory of that corresponding method, then it is implemented in Microsoft Excel, and finally, we will code the considered example using Matlab language programming.In Summary, we will discuss the following points and MCDM approaches in detail:1- Background of MCDMs2- Simple Additive Weightage (SAW)3- Analytic Hierarchy Process (AHP)4- Analytic Network Process (ANP)5- Technique for Order Preference and Similarity to Ideal Solution (TOPSIS)6- Elimination Et Choice Translating Reality (ELECTRE)7- Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)8- VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)9- Decision-Making Trial and Evaluation Laboratory (DEMATEL)10- Grey Relational Analysis (GRA)11- Multi-objective Optimization on the Basis of Ratio Analysis Method (MOORA)12- Complex Proportion Assessment Method (COPRAS)13- Additive Ratio Assessment Method (ARM-ARAS)14- Weighted Aggregated Sum Product Assessment (WASPAS)15- Stepwise Weight Assessment Ratio Analysis (SWARA)16- COmbinative Distance-based ASsessment (CODAS)17- Evaluation Based on Distance from Average Solution (EDAS)18- Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS)19- CRiteria Importance Through IntercriteriaCorrelation (CRITIC)20- Entropy Weighting Technique21. Combined Compromise Solution (CoCoSo)This course also includes a large number of coding videos to give you enough opportunity to practice the theory covered in the lecture.By passing this course, you will become aware of how to use Excel and Matlab on a wide range of MCDM problems, and as a result, you will learn how to handle other MCDM approaches.Please note that this course will be updated with new MCDM approaches.

Overview

Section 1: Background of MCDMs

Lecture 1 Introduction

Section 2: Simple Additive Weightage (SAW)

Lecture 2 Introduction

Lecture 3 Example 1

Lecture 4 Example 2

Lecture 5 Example 3

Section 3: Analytic Hierarchy Process (AHP)

Lecture 6 Introduction

Lecture 7 Example 1

Lecture 8 A framework for AHP

Lecture 9 Coding AHP

Lecture 10 Example 2

Lecture 11 Example 3

Section 4: Analytic Network Process (ANP)

Lecture 12 Introduction

Lecture 13 Using Supermatrix in AHP-Example 1

Lecture 14 Using Supermatrix in AHP-Example 2

Lecture 15 ANP-Example 1

Lecture 16 ANP-Example 2

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

Lecture 17 Introduction

Lecture 18 Implementation of TOPSIS in Excel

Lecture 19 Implementation of TOPSIS in Matlab

Section 6: Elimination Et Choice Translating Reality (ELECTRE)

Lecture 20 Introduction

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 Introduction

Lecture 24 Implementation of PROMETHEE in Excel

Lecture 25 Implementation of PROMETHEE in Matlab

Section 8: VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)

Lecture 26 Introduction

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 Introduction

Lecture 30 Implementation of DEMATEL in Excel

Lecture 31 Implementation of DEMATEL in Matlab

Section 10: Grey Relational Analysis (GRA)

Lecture 32 Introduction

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

Lecture 36 Implementation of MOORA in Excel

Lecture 37 Implementation of MOORA in Matlab

Section 12: Complex Proportion Assessment Method (COPRAS)

Lecture 38 Introduction

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

Lecture 42 Implementation of ARAS in Excel

Lecture 43 Implementation of ARAS in Matlab

Section 14: Weighted Aggregated Sum Product Assessment (WASPAS)

Lecture 44 Introduction

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

Lecture 48 Implementation of SWARA in Excel

Lecture 49 Implementation of SWARA in Matlab

Section 16: COmbinative Distance-based ASsessment (CODAS)

Lecture 50 Introduction

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

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

Lecture 57 Implementation of MARCOS in Excel

Lecture 58 Implementation of MARCOS in Matlab

Section 19: CRiteria Importance Through Intercriteria Correlation (CRITIC)

Lecture 59 Introduction

Lecture 60 Implementation of CRITIC in Excel

Lecture 61 Implementation of CRITIC in Matlab

Section 20: Entropy

Lecture 62 Introduction

Lecture 63 Implementation of Entropy in Excel

Lecture 64 Implementation of Entropy in Matlab

Section 21: Combined Compromise Solution (CoCoSo)

Lecture 65 Introduction

Lecture 66 Implementation of CoCoSo in Excel

Lecture 67 Implementation of CoCoSo in Matlab

Section 22: Fuzzy AHP

Lecture 68 An Introduction to Chang extension method

Anyone who wants to learn Multi-Criteria Decision Making (MCDM) approaches,Anyone who wants to solve MCDM problms via Excel,Anyone who wants to code MCDM problms in Matlab

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