Strategic Approach in Multi-Criteria Decision Making (eBook)

A Practical Guide for Complex Scenarios

Nolberto Munier, Eloy Hontoria, Fernando Jiménez-Sáez (Autoren)

eBook Download: PDF

2019 | 2019
XV, 273 Seiten
Springer International Publishing (Verlag)
978-3-030-02726-1 (ISBN)

Infos

This book examines multiple criteria decision making (MCDM) and presents the Sequential Interactive Modelling for Urban Systems (SIMUS) as a method to be used for strategic decision making. It emphasizes the necessity to take into account aspects related to real world scenarios and incorporating possible real life aspects for modelling. The book also highlights the use of sensitivity analysis and presents a method for using criteria marginal values instead of weights, which permits the drawing of curves that depicts the variations of the objective function due to variations of these marginal values. In this way it also gives quantitative values of the objective function allowing stakeholders to perform a comprehensive risk analysis for a solution when it is affected by exogenous variables. 

Strategic Approach in Multi-Criteria Decision Making: A Practical Guide for Complex Scenarios is divided into three parts. Part 1 is devoted to exploring the history and development of the discipline and the way it is currently used. It highlights drawbacks and problems that scholars have identified in different MCDM methods and techniques. Part 2 addresses best practices to assure quality MCDM process. Part 3 introduces the concept of Linear Programming and the proposed SIMUS method as techniques to deal with MCDM. It also includes case studies in order to help document and illustrate difficult concepts, especially related to demands from a scenario and also in their modelling. The decision making process can be a complex task, especially with multi-criteria problems. With large amounts of information, it can be an extremely difficult to make a rational decision, due to the number of intervening variables, their interrelationships, potential solutions that might exist, diverse objectives envisioned for a project, etc. The SIMUS method has been designed to offer a strategy to help organize, classify, and evaluate this information effectively.

Eloy Hontoria Industrial Engineer- Polytechnic University of Valencia - Spain. Ph.D. degree in Economics and Business Administration (International Doctorate Mention). Extensive experience in Operations Management and Supply Chain at the Logistic Department in different local and multinational firms, being in charge of Logistics activities, as well as CEO of his own companies. Currently working at the Department of Business Economics at the Polytechnic University of Cartagena - Spain. His research interests refer to Operation Research, IT, Governance and Business Informatics. 

Fernando Jiménez Sáez Ba. degree in Economics - Universidad Autónoma de Madrid (SPAIN) Ma. degree in Analysis and Management of Science and Technology - Universidad Carlos III de Madrid (SPAIN) Ma. degree in Economics of Technical Change - Maastricht University (THE NETHERLANDS) Ph.D. degree in Engineering Projects Management - Universitat Politècnica de València, (SPAIN) Tenured university professor in the Department of Engineering Projects of the Polytechnic University of Valencia. Researcher at INGENIO (CSIC-UPV) (Spanish Council of Scientific Research and Polytechnic University of Valencia). He has worked on numerous European, national and regional projects and contracts. Currently leading several projects and contracts with European and Latin American partners to study and diagnose the capabilities of Regional Innovation Systems.

Nolberto Munier Mechanical Engineer- National University of Córdoba - Argentina. Master degree in Project Management- Polytechnic University of Valencia- Spain. Ph.D. degree in Project Management - Polytechnic University of Valencia. Extensive experience in large projects in Argentina, Perú, Ecuador, México, USA and Canada. Author of 27 books on Operations Research, Project Management, Multi Criteria Decision-Making, and Risk Analysis, as well as more than 100 papers. Associate Researcher - INGENIO (CSIC-UPV) (Spanish Council of Scientific Research and Polytechnic University of Valencia). Currently a technical writer and reviewer of technical articles for several journals.

Inhalt

Preface and Road Map 5
Book Structure 5
Introduction 7
Contents 12
Part I: History of MCDM and How It Is Performed 13
Chapter 1: Multi-Criteria Decision-Making, Evolution and Characteristics 14
1.1 History and Evolution of  Multi-Criteria Decision-Making Methods 14
1.1.1 Some Background Information on Decision-Making 14
1.2 Introduction to Most Common and Used Heuristic Methods 18
1.3 The Decision-Making Paradox 19
1.4 Which Is the Best MCDM Method? 21
1.5 Considering and Modelling Reality 22
1.6 Is It Possible to Represent Reality Faithfully? 22
1.7 Conclusion of This Chapter 24
References 24
Chapter 2: The Initial Decision Matrix (IDM) and Its Fundamental Role in Modelling a Scenario 25
2.1 Basic Components of the Initial MCDM Decision Matrix 25
2.1.1 Stakeholders 25
2.1.2 Decision-maker or Group of DMs 25
2.1.3 Objective(s) that the Scenario Must Attain 26
2.1.4 Scenario(s) 26
2.1.5 Alternatives, Projects or Options 26
2.1.6 Criteria 26
2.1.6.1 Areas Included in Criteria 27
Areas 27
2.1.6.2 Capacity of Criteria to Evaluate Alternatives 31
2.1.6.3 Actions for Criteria 33
2.1.6.4 Resources and Restrictions for Criteria 34
2.1.6.5 Criteria Duality 34
2.1.7 Performance Values 34
2.1.8 Decision Matrix 34
2.1.9 Methods 35
2.2 Routines to Perform with Data 35
2.2.1 Normalization 35
2.3 Rank Reversal 36
2.3.1 Possible Causes for RR 39
2.3.2 Brief Information on Rank Reversal in Different MCDM Methods 41
2.3.2.1 Rank Reversal in AHP 41
2.3.2.2 Rank Reversal in TOPSIS 42
2.3.2.3 Rank Reversal in PROMETHEE 42
2.3.2.4 Rank Reversal in ELECTRE 42
2.3.2.5 Rank Reversal in SAW 42
2.4 The Uncertain Best Solution 42
2.5 Characteristics of Components of the Initial Decision Matrix (IDM) 43
2.5.1 The MCDM Process as a System 43
2.5.2 Alternatives Relationships 43
2.5.3 Alternatives Heavily Related: A Case – Selecting Proposals 45
2.5.4 Including and Excluding Alternatives: Conditions by a Third Party 45
2.5.4.1 Actual Cases 45
2.5.5 Forced Alternatives: An Actual Case – Fulfilment of Previous Commitments 46
2.5.6 Criteria Selection 47
2.5.7 Resources: An Actual Case – Oil Refinery 47
2.5.8 Criteria Range 48
2.5.9 Annual Budget Restriction: An Actual Case – 5-Year Development Plan 48
2.5.10 Criteria Correlation 49
2.5.11 Risk: A Fundamental Criterion 49
2.5.12 Examining Differences in Results for the Same Problem Between Assumed Weights and Weights from Entropy: Case Study – Electrical Transmission Line 52
2.5.13 Working with a Variety of Performance Values: An Actual Case – Environmental Indicators 55
2.5.14 The ‘Z’ Method for Determining Some Performance Values for Qualitative Criteria 55
2.5.15 The Z Matrix: Case Study – Determining Risk Performance Values for Inputting in Risk Criteria 58
2.5.16 Need to Work with Performance Values Derived from Another Data Table 61
2.5.17 Conditioning the Decision Matrix to Obtain a Specified Number of Results 61
2.6 Additional Conditions Required for Methods 62
2.7 Sensitivity Analysis 62
2.7.1 The Two Types of Sensitivity Analysis 63
2.7.2 A Critical Analysis of the Way Sensitivity Analysis Is Performed Nowadays 64
2.8 Conclusion of This Chapter 67
References 68
Part II: What Should Be Done in the MCDM Process 70
Chapter 3: How to Shape Multiple Scenarios 71
3.1 Introduction 71
3.2 Developing the Best Strategy: Case Study – Selecting Projects for Agribusiness Activities in Different Scenarios 73
3.3 Solving the Problem 78
3.4 Conclusion of This Chapter 79
References 79
Chapter 4: The Decision-Maker, A Vital Component of the Decision-Making Process 80
4.1 Decision-Maker (DM) Functions: Interpretation of Reality 80
4.1.1 First Level: Building the Initial Decision Matrix 80
4.1.2 Second Level: Selecting a Method to Use 83
4.1.3 Third Level: Following the Computing Process 84
4.1.4 Fourth Level: Examining the Result 84
4.1.5 Synergy Between the DM and the Model 85
4.2 Conclusion of This Chapter 86
References 86
Chapter 5: Design of a Decision-Making Model Reality-Wise: How Should It Be Done? 87
5.1 Modelling 87
5.2 Interpreting Reality 91
5.2.1 Areas Where Reality Is Not in General Interpreted 92
5.2.1.1 Scenarios 92
5.2.1.2 Alternatives 93
5.2.1.3 Criteria 93
5.2.1.4 Performance Values 94
5.2.1.5 Results Delivered by MCDM Methods 94
5.3 Check List for Aspects to Be Normally Considered When Modelling 95
5.4 Working Template for Modelling a Scenario in MCDM and for Selecting a Method to Solve It 96
5.5 Conclusion of This Chapter 103
References 104
Part III: Proposing the SIMUS Method for a Strategic Procedure to Manage Real-World Scenarios 105
Chapter 6: Linear Programming Fundamentals 106
6.1 Basic Mathematical Background 107
6.2 The Initial Decision Matrix (IDM) 108
6.3 Solving the LP Problem Graphically: Case Study – Power Plant Based in Solar Radiation 109
6.4 The Two Sides of a Coin 113
6.5 Description of the Method 114
6.6 Graphical Explanation of Correlation 114
6.7 Is Rank Reversal Present in Linear Programming? 120
6.8 Conclusion of This Chapter 121
References 121
Chapter 7: The SIMUS Method 122
7.1 Background Information 123
7.2 How SIMUS Works: Case Study – Power Plant Based in Solar Radiation 125
7.2.1 Normalization by SIMUS 131
7.3 SIMUS Application Example: Case Study – Power Plant Based in Solar Radiation 133
7.4 Special Circumstances 137
7.4.1 Ties in Scores 137
7.4.2 Need to Use Formulae for Performance Factors 138
7.4.3 Errors in the Decision Matrix 140
7.4.4 Dealing with Non-lineal Criteria 143
7.5 Is SIMUS Affected by Rank Reversal? 146
7.6 Testing SIMUS in Rank Reversal 147
7.6.1 Case 1: Investment in Renewable Sources of Energy 148
7.6.2 Case 2: Rehabilitation of Abandoned Urban Land 151
7.6.3 Case 3: Determining Sustainable Indicators 153
7.6.4 Conclusion of This Section 157
7.7 Solving Multi-scenarios Simultaneously 157
7.7.1 Analysis of Global Solution: What to Produce and Where? 158
7.7.2 What Projects Go into Each Scenario 159
7.8 Conclusion of This Chapter 161
References 161
Chapter 8: Sensitivity Analysis by SIMUS: The IOSA Procedure 163
8.1 Background Information 163
8.1.1 Example: Agroindustry for Export 165
8.2 Data that the DM Must Input in IOSA 168
8.3 DM Analysis 170
8.4 Sequence for Sensitivity Analysis by SIMUS/IOSA 171
8.5 Report to Stakeholders: Type of Concerns and Questions Expressed by the Stakeholders Relative to This Production Problem and DM Answers 173
8.6 Conclusion of This Chapter 175
References 175
Chapter 9: Group Decision-Making: Case Study – Highway Construction 177
9.1 Background Information 177
9.2 Construction of the Decision Matrix: A Case – Construction of a Highway in China 178
9.3 Loading Data into SIMUS 180
9.4 Step-by-Step Analysis 181
9.5 Detailed Analysis by the Group 182
9.5.1 First Objective (Minimize Construction Cost) 182
9.5.2 Second Objective (Minimize Maintenance Cost) 184
9.5.3 Third Objective (Minimize Delays in Transit) 185
9.5.4 Fourth Objective (Maximize Safety) 185
9.5.5 Fifth Objective (Maximize Lighting) 186
9.5.6 Sixth Objective (Minimize Breaking Connectivity Between Areas due to the Highway) 187
9.5.7 Seventh Objective (Minimize Construction Time) 188
9.5.8 Eighth Objective (Environmental Impacts) 188
9.5.9 Ninth Objective (Minimize Traffic Noise) 189
9.6 Conclusion of This Chapter 191
References 192
Chapter 10: SIMUS Applied to Quantify SWOT Strategies 193
10.1 Background 193
10.2 Procedure 194
10.3 Application Example: Strategy for Fabricating Electric Cars (Case Study) 197
10.4 Construction of the Numerical SWOT Matrix 197
10.4.1 Market and Government 197
10.5 Preparing an Excel Matrix with Data 202
10.6 Discussion 204
10.7 Conclusion of This Chapter 205
References 206
Chapter 11: Analysis of Lack of Agreement Between MCDM Methods Related to the Solution of a Problem: Proposing a Methodology for Comparing Methods to a Reference 207
11.1 Objective of This Section 207
11.2 Causes for Discrepancies on Results 208
11.3 Subjective Preferences 209
11.3.1 Subjective Weights 209
11.3.2 Objective Weights 210
11.3.3 Inconsistencies 211
11.3.4 Evaluating Results 211
11.3.5 The Proxy Approach 212
11.3.6 Selecting a MCDM Method 213
11.3.7 The DM Role 215
11.3.8 What MCDM Method Can Be Chosen as a Proxy? 215
11.3.9 Measuring Similitude Between Rankings 217
11.3.10 Example as How Rankings Can Be Compared 219
11.4 Conclusion of This Chapter 221
References 222
Chapter 12: Some Complex and Interesting Cases Solved by SIMUS 224
12.1 Case Study: Simultaneous Multiple Contractors Selection for a Large Construction Project 224
12.1.1 Background Information 224
12.1.2 The Case: Construction of a Large Power Plant 226
12.1.2.1 Analysis 231
12.1.3 Conclusion of This Case 235
12.2 Case Study: Quantitative Evaluation of Government Policies Regarding Penetration of Advanced Technologies 235
12.2.1 Background Information 235
12.2.2 Process Structure 235
12.2.3 The Case 236
12.2.4 Analysis of Different Policies 245
12.2.5 Conclusion of This Case 246
12.3 Case Study: Selecting Hydroelectric Projects in Central Asia 246
12.3.1 Background Information 246
12.3.2 Conclusion of This Case 249
12.4 Upgrading Infrastructure: Case Study – Community Infrastructure Upgrading for Villages in Ghana 249
12.4.1 Background Information 250
12.4.2 Areas and Data 250
12.4.2.1 Analysis 252
Regarding People 252
Regarding Hectares 253
Regarding Costs 254
12.4.3 Conclusion for This Case 254
12.5 Case Study: Urban Development Study for the Extended Urban Zone of Guadalajara, According to Sustainability Indicators, Mexico 254
12.5.1 Background Information 255
12.5.1.1 Projects 256
12.5.1.2 Criteria 257
12.5.1.3 Project by Municipalities 258
12.5.1.4 Projects that are Shared for More than One Municipality 258
12.5.1.5 Maximum Amounts Available for Municipality 259
12.5.1.6 Result 259
12.5.2 Conclusion of This Case 259
12.6 Case Study: Selection of the Best Route Between an Airport and the City Downtown 260
12.6.1 Background Information 261
12.6.2 The Case 261
12.6.3 Conclusion of This Case 264
References 264
A. Appendix 266
A.1. The Simplex Algorithm: Its Analysis – Progressive Partial Solutions 266
A.2. Practical Demonstration of Absence of Rank Reversal in SIMUS 271
A.2.1. Original Ranking 272
A.2.2. Adding Project P6 = Project P1 (Table A.7) 272
A.2.3. Adding Project P6 Considered the Worst of All (Table A.8) 272
A.2.4. Adding Project P7 = P1 and Keeping Project P6= P2 (Table A.9) 273
A.2.5. Adding Project P6 Considered the Best of All (Table A.10) 273
A.2.6. Deleting Project P4 (Table A.11) 273
A.2.7. Deleting Projects P1 and P5 Simultaneously (Table A.12) 274
A.2.8. Verifying Transitivity 274
A.2.9. Verifying Transitivity (Table A.14) 274
A.2.10. Summary of Scenarios and Results 275
Conclusion 276
References 276

Produkt-Details

• Erscheint lt. Verlag: 29.1.2019
• Reihe/Serie: International Series in Operations Research & Management Science
• Reihe/Serie: International Series in Operations Research & Management Science
• Zusatzinfo: XV, 273 p. 109 illus., 67 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Planung / Organisation
• Schlagworte: Decision Making • Input-Output Sensitivity Analysis • IOSA • MCDA • MCDM • Multi-criteria decision analysis • multi-criteria decision making • Multiple Criteria Decision Making • Operations Research • Sensitivity Analysis • Sequential Interactive Modelling for Urban Systems • SIMUS
• ISBN-10: 3-030-02726-0 / 3030027260
• ISBN-13: 978-3-030-02726-1 / 9783030027261