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Multi-criteria Decision Analysis Methods and Software

by: Ishizaka, Alessio; Nemery, Philippe

Multi-criteria Decision Analysis Methods and Software

by: Ishizaka, Alessio; Nemery, Philippe

ISBN 13:
9781119974079
ISBN 10:
1119974070
Edition: 1st
Format: Hardcover
Copyright: 08/19/2013
Publisher: Wiley

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Multi-criteria Decision Analysis Methods and Software > ISBN13: 9781119974079

Summary

This book presents an introduction to MCDA followed by more detailed chapters about each of the leading methods used in this field. Comparison of methods and software is also featured to enable readers to choose the most appropriate method needed in their research.

Worked examples as well as the software featured in the book are available on an accompanying website.

Author Biography

Alessio Ishizaka
Reader in Decision Analysis, Portsmouth Business School, University of Portsmouth, UK

Philippe Nemery
Senior Research Scientist, SAP Labs – China, Shanghai, PRC

FOREWORD

ACKNOWLEDGEMENTS

1. GENERAL INTRODUCTION

1.1. INTRODUCTION

1.2. DECISIONS PROBLEMS

1.3. MCDA METHODS

1.4. MCDA SOFTWARE

1.5. SELECTION OF MCDA METHODS.

1.6. OUTLINE OF THE BOOK

1.7. REFERENCES

2. AHP – ANALYTIC HIERARCHY PROCESS

2.1. INTRODUCTION

2.2. ESSENTIAL CONCEPTS OF AHP

2.2.1. Problem structuring

2.2.2. Priority Calculation

2.2.3. Consistency check

2.2.4. Sensitivity analysis

2.3. AHP SOFTWARE

2.3.1. MakeItRational

2.4. IN THE BLACK BOX OF AHP

2.4.1. Problem structuring

2.4.2. Judgement scales

2.4.3. Consistency

2.4.4. Priorities derivation

2.4.5. Aggregation

2.5. EXTENSIONS OF AHP

2.5.1. Analytic Hierarchy Process Ordering

2.5.2. Group Analytic Hierarchy Process (GAHP)

2.5.3. Clusters and pivots for a large number of alternatives

2.5.4. AHPSort

2.6. REFERENCES

3. ANP – ANALYTIC NETWORK PROCESS

3.1. INTRODUCTION

3.2. ESSENTIAL CONCEPTS OF ANP

3.2.1. Inner dependency in the criteria cluster

3.2.2. Inner dependency in the alternative cluster

3.2.3. Outer dependency

3.2.4. Influence matrix

3.3. ANP SOFTWARE

3.3.1. SuperDecisions

3.4. IN THE BLACK BOX OF ANP

3.4.1. Markov chain

3.4.2. Supermatrix

3.5. REFERENCES

4. MAUT - MULTI ATTRIBUTE UTILITY THEORY

4.1. INTRODUCTION

4.2. ESSENTIAL CONCEPTS OF MAUT

4.3. ESSENTIALS OF THE MAUT METHOD

4.3.1. The additive model

4.4. RIGHTCHOICE

4.4.1. Data and utility functions

4.4.2. Results

4.4.3. Sensitivity Analysis

4.4.4. Group decision or Multi scenario analysis

4.5. IN THE BLACK BOX OF MAUT

4.6. EXTENSIONS OF THE MAUT METHOD

4.6.1. The UTA method

5.2. ESSENTIAL CONCEPTS OF MACBETH

5.2.1. Problem structuring : value tree

5.2.2. Score calculation

5.2.3. Incompatibility check

5.3. SOFTWARE DESCRIPTION

5.3.1. M-MacBeth

5.4. IN THE BLACK BOX OF MACBETH

6.2. ESSENTIAL CONCEPTS OF THE PROMETHEE METHOD

6.2.1. Unicriterion preference degrees

6.2.2. Unicriterion Positive, Negative and Net flows

6.2.3. Global flows

6.2.4. The Gaia Plane

6.2.5. Sensitivity analysis

6.3. THE SMART-PICKER PRO SOFTWARE

6.3.1. Data entry

6.3.2. Preference parameters entering

6.3.3. Weights

6.3.4. Promethee II ranking

6.3.5. Gaia Plane

6.3.6. Sensitivity analysis

6.4. IN THE BLACK BOX OF PROMETHEE

6.4.1. Unicriterion preference degrees

6.4.2. Global preference degree

6.4.3. Global Flows

6.4.4. Promethee I and Promethee II ranking

6.4.5. Gaia

6.4.6. Influence of pairwise comparisons

6.5. EXTENSIONS OF PROMETHEE

6.5.1. PROMETHEE GDSS: Group Decision Support System

6.5.2. FlowSort: a sorting or supervised classification method

6.6. REFERENCES

7. ELECTRE

7.1. INTRODUCTION

7.2. ESSENTIALS OF THE ELECTRE METHODS

7.2.1. Electre III

7.3. THE ELECTRE III-IV SOFTWARE

7.3.1. Data entering

7.3.2. Preference parameters entering

7.3.3. Results

7.4. IN THE BLACK BOX OF ELECTRE III

7.4.1. Outranking relations

7.4.2. Partial concordance degree

7.4.3. Global concordance degree

7.4.4. Partial discordance degree

7.4.5. Outranking degree

7.4.6. Partial ranking: exploitation of the outranking relations

7.4.7. Some properties

7.5. ELECTRE TRI

7.5.1. Introduction

7.5.2. Preference relations

7.5.3. Assignment Rules

8.2. ESSENTIALS OF TOPSIS

8.3. REFERENCES

9. GOAL PROGRAMMING

9.1. INTRODUCTION

9.2. ESSENTIAL CONCEPTS OF GOAL PROGRAMMING

9.3. SOFTWARE DESCRIPTION

9.3.1. Microsoft Excel Solver

9.4. EXTENSIONS OF THE GOAL PROGRAMMING

9.4.1. WEIGHTED GOAL PROGRAMMING

9.4.2. LEXICOGRAPHIC GOAL PROGRAMMING

9.4.3. CHEBYSHEV GOAL PROGRAMMING

9.5. REFERENCES

10. DEA - DATA ENVELOPMENT ANALYSIS

10.1. INTRODUCTION

10.2. ESSENTIAL CONCEPTS OF DEA

10.2.1. An efficiency measurement method

10.2.2. A DEA case study

10.2.3. Multiple outputs and inputs

10.2.4. Types of efficiency

10.2.5. Managerial implications

10.3. DEA SOFTWARE

10.4. DEA IN THE BLACK BOX

10.4.1. Constant returns to scale

10.4.2. Variable returns to scale

10.5. EXTENSIONS OF DEA

10.5.1. Adjusting for the environment

10.5.2. Preferences

10.5.3. Sensitivity analysis

10.5.4. Time series data

10.6. REFERENCES

11. MULTI-METHODS PLATFORMS

11.1. INTRODUCTION

11.2. DECISION DECK

11.3. DECERNS

11.3.1. The GIS module

11.3.2. The MCDA module

11.3.3. THE GDSS Module

11.3.4. Integration

11.4. REFERENCES

12. APPENDIX: LINEAR OPTIMISATION

12.1. PROBLEM MODELLING

12.2. GRAPHICAL SOLUTION

12.3. SOLUTION WITH MICROSOFT EXCEL

INDEX

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