Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems (eBook)

Preface 5
Contents 8
Contributors 12
Part I Multiple Criteria Decision Making, Transportation, Energy Systems, and the Environment 18
On the Potential of Multi-objective Optimization in the Design of Sustainable Energy Systems 19
1 Introduction 19
2 Methodology 20
3 Real-world Applications 22
3.1 Communal Energy System 22
3.2 Industrial Energy System 23
3.3 Distillation Plant 25
4 Conclusions and Potential 26
References 27
Evaluation of the Significant Renewable Energy Resources in India Using Analytical Hierarchy Process 29
1 Introduction 29
2 Nonrenewable Energy Resources and Demands in India 30
3 Government Initiatives to Promote Renewable Energy 31
3.1 Wind Energy 32
3.2 Bio Energy 32
3.3 Solar Energy 32
4 AHP Approach 33
5 Application of AHP for the Prioritization of Renewable Energy Resources in India 36
5.1 Synthesizing Judgments 40
6 Conclusion 41
References 41
Multiple Criteria Decision Support for Heating Systemsin Electric Transport 43
1 Environmental Parameters 43
2 Problem Formulation 44
3 HVAC System 44
4 Control System Design 45
5 Decision Algorithm 46
6 Decision Methodology 47
7 System Decision Making Control Realization 48
8 Conclusions 50
References 50
Multi Criteria Decision Support for Conceptual Integral Design of Flex(eble)(en)ergy Infrastructure 51
1 Introduction 51
2 Design Methodology 53
2.1 Integral Design 53
2.2 Morphological Overview 54
2.3 Evaluation and Decision Making VDI 2225Decision Support
3 MCDM 56
3.1 Multi-Criteria Decision Analysis in Energy Planning 57
3.2 MCDA and Planning of Local Energy Systemswith Multiple Energy Carriers 57
3.3 MCDA and Energy Planning – a Review 58
4 Discussion 58
5 Conclusion and Further Research 59
References 60
A Multi Criteria Knapsack Solution to Optimise Natural Resource Management Project Selection 62
1 Introduction 62
2 Compromise Programming 63
3 The Knapsack Problem (KP) 64
4 Exact Solution Methods and Meta-Heuristics 65
5 Development of the Multi Criteria Analysis Tool (MCAT) 65
6 Illustrative Application of MCAT – Water Quality Investments Perth, Western Australia 66
7 Conclusion 69
References 69
Environmental and Cost Synergy in Supply Chain Network Integration in Mergers and Acquisitions 71
1 Introduction 71
2 The Pre- and Post-Merger Supply Chain Network Models 74
2.1 The Pre-Merger Supply Chain Network Modelwith Environmental Concerns 74
2.2 The Post-Merger Supply Chain Network Modelwith Environmental Concerns 78
3 Quantifying Synergy Associated with Multicriteria Decision-Making Firms with Environmental Concernsin Mergers/Acquisitions 81
4 Numerical Examples 82
5 Summary and Concluding Remarks 88
References 89
The Analytic Hierarchy Process in the Transportation Sector 93
1 Introduction 93
2 Benefits and Costs in Crossing a River 97
2.1 Benefits 98
2.2 Costs 98
2.3 Results 99
3 Planning, Sudan Transport 100
3.1 Priorities of the Scenarios 100
3.2 Priorities of Regions and Projects 101
4 Dependence and Feedback in Choosing a Car 102
5 Conclusions 105
References 105
RECIFE: A MCDSS for Railway Capacity Evaluation 106
1 Introduction 106
2 Railway Capacity Evaluation Problem 108
3 Organization and Components of RECIFE 109
3.1 Data in Input: One Scenario 110
3.2 Handling the First Criterion: The Optimization Stage 111
3.3 Handling the Second Criterion: The Simulation Stage 111
3.4 Data in Output: A Timetable 114
4 Conclusion and On-going Works 114
References 115
Balancing Efficiency and Robustness – A Bi-criteria Optimization Approach to Railway Track Allocation 117
1 Introduction 118
2 The Track Allocation Problem 119
3 Towards a Bi-criteria Optimization Approach 121
4 Details on Column Generation 124
5 Preliminary Computational Results 125
References 127
Tolling Analysis with Bi-objective Traffic Assignment 129
1 Introduction 129
2 Conventional Traffic Assignment 133
3 Bi-objective Traffic Assignment Procedures 134
4 Feasibility of Transformation of BUE Conditions 137
5 Conclusions and Further Research 140
References 141
Part II Applications of Multiple Criteria Decison Makingin Other Areas 142
National Risk Assessment in The Netherlands 143
1 Introduction 143
2 The NRA 144
3 Multi-Criteria Methods Used 146
3.1 The Weighted Sum Approach 147
3.2 The Medal Methods 147
3.3 The Evamix Method 148
4 Uncertainty, Sensitivity and Robustness Analyses 149
4.1 Pluralistic Weighting: Using Group Preference Profiles 149
4.2 Traditional Uncertainty, Sensitivity, and Robustness Analyses 150
5 NRA Outcomes, Use and Communication 151
6 Concluding Remarks 152
References 153
Evaluation of Green Suppliers ConsideringDecision Criteria Dependencies 154
1 Introduction 154
2 Green Supplier Selection Model 155
3 Choquet Integral Based Aggregation 158
4 Application 160
5 Conclusion 162
References 162
A Multiobjective Bilevel Program for Production-Distribution Planning in a Supply Chain 164
1 Introduction 164
2 Bilevel Programs with a Single Objective Functionat each Level 166
2.1 Main Characteristics 166
2.2 Bilevel Problems Versus Bicriteria Problems 167
3 Bilevel Programs with Multiple Objective Functionsat the Lower-Level 168
4 Modeling Production-Distribution Planning in a Supply Chain as a Bilevel Program 170
4.1 The Objective Functions and the Constraints 171
4.2 The Models 172
5 Conclusions 173
References 174
An Ordinal Regression Method for Multicriteria Analysis of Customer Satisfaction 175
1 Introduction 175
2 The Dummy Variable Regression Method 176
3 Results 180
4 Conclusions 182
References 183
Discrete Time-Cost Tradeoff with a Novel Hybrid Meta-Heuristic 185
1 Introduction 185
2 Preliminaries and Definitions 187
2.1 Structure of a Solution 187
2.2 Initial Population 187
2.3 Time-Cost Tradeoff Curve and Convex Hull 187
2.4 Distance Measurement 188
2.5 Crossover 188
2.6 Mutation 189
2.7 Simulated Annealing 189
3 Hybrid Meta-Heuristic 189
4 HMH Approach to Test Cases 191
5 Conclusions 194
References 196
Goal Programming Models and DSS for Manpower Planning of Airport Baggage Service 197
1 Introduction 197
2 Data Modeling 198
2.1 Individual Demands 199
2.2 Overall Demands 199
3 Goal Program Modeling 200
3.1 Model 1 200
3.2 Model 2 202
3.3 Model 3 202
4 Numerical Results and Comments 203
4.1 Numerical Results 203
4.2 Analytical Comments and Comparison of the Models 204
5 Decision Support System 205
6 Concluding Remarks 206
References 207
A MCDM Tool to Evaluate Government Websitesin a Fuzzy Environment 208
1 Introduction 208
2 An Evaluation Framework for Government Websites 209
2.1 Website Evaluation Criteria 210
2.2 Fuzzy Delphi Method 211
2.3 Fuzzy VIKOR Method 212
3 Case Study: Implementation of the Proposed Framework 213
4 Concluding Remarks 216
References 216
Investigating Coverage and Connectivity Trade-offsin Wireless Sensor Networks: The Benefits of MOEAs 218
1 Introduction 218
2 Related Work 219
3 Problem Formulation 220
3.1 Model Description 220
3.2 Optimization Criteria 220
3.2.1 Sensor Cost 220
3.2.2 Transmission Failure Probability 221
4 An Evolutionary Multiobjective Algorithm with Variable Length Representations 221
4.1 Representation 222
4.2 Initialization 222
4.3 Crossover 223
4.4 Voronoi Mutation 223
4.5 Gaussian Position Mutation 224
5 Results and Discussion 224
6 Conclusion and Outlook 226
References 227
AHP as an Early Warning System:An Application in Commercial Banks in Turkey 229
1 Introduction 230
2 Historical Background of Turkish Banking Sector 230
3 Research Background in the Banking Sector 231
4 The Methodology and Data 232
4.1 Analytical Hierarchy Process 232
4.2 Experts 233
4.3 Main Criteria and Descriptions 233
4.4 Implementation of AHP and Results 235
5 Sensitivity Analysis 236
6 Conclusion 237
References 238
A Multi-Criteria Evaluation of Factors Affecting InternetBanking in Turkey 240
1 Introduction 240
2 Previous Research 241
3 Proposed MCDA Framework 243
3.1 Structuring the Problem 243
3.2 Constructing the Decision Model 244
3.3 Analyzing the Problem 246
4 A Real-Life Application 246
5 Conclusions and Further Suggestions 248
References 249
Part III Theory and Methodology of Multiple CriteriaDecision Making 252
Priority Elicitation in the AHP by a Pareto Envelope-Based Selection Algorithm 253
1 Introduction 253
2 The Prioritization Problem in the AHP 254
3 A Multiobjective Approach to Prioritization 255
3.1 Optimization Criteria 255
3.2 The TOP Problem 256
4 Solving the TOP Problem by Evolutionary Computing 257
4.1 Multiobjective Algorithms 257
4.2 Pareto Envelope-Based Selection Algorithm-II (PESA-II) 257
5 An Illustrative Example 259
6 Conclusions 260
References 261
Bibliometric Analysis of Multiple Criteria Decision Making/Multiattribute Utility Theory 262
1 Introduction 262
2 Bibliometric Data 263
3 Conclusions 271
References 271
Ordinal Qualitative Scales 272
1 Introduction 272
2 Preference Relation Representable by a QualitativeOrdinal Scale 273
3 Strength of Preference Representable by a Qualitative Ordinal Scale 275
4 Preferences on Single Criteria Representableby a Qualitative Ordinal Scale 276
5 Strength of Preference on Single Criteria Representableby a Qualitative Ordinal Scale 277
6 Conclusions 278
References 278
Multi-objective Model Predictive Control 280
1 Introduction 280
2 Model Prediction Using Support Vector Regression 281
3 Aspiration Level Approach to Interactive Multi-objective Optimization 283
4 Multi-objective Model Predictive Control 284
5 Illustrative Example – Rocket Soft Landing Problem 285
6 Concluding Remarks 288
References 289
Multiple Criteria Nonlinear Programming Classification with the Non-additive Measure 291
1 Introduction 291
1.1 Multiple Criteria Linear/Nonlinear Programming 292
1.2 Non-additive Measure 294
2 Multiple Criteria Nonlinear Programming with Non-additive Measure 294
2.1 The Choquet Integral with Respect to Non-additive Measure 294
2.2 Non-additive Measure for Data Modeling in Classification 296
3 Experimental Results 297
3.1 US Credit Cardholders' Behaviors Classification 297
3.2 German Credit Cardholders' Behaviors Classification 298
4 Conclusions 298
References 299
Part IV Multiple Objective Optimization 300
A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization 301
1 Introduction 301
2 Related Work 302
3 Multi-objective Optimization and SMS-EMOA 303
4 Self-Adaptation 304
5 Kriging 305
6 Steady-State Parallelization 306
7 Application on Test Problems and in Molecular Control 307
8 Conclusion 310
References 310
Faster Hypervolume-Based Search Using Monte Carlo Sampling 312
1 Introduction 312
2 Preliminaries 313
3 Approach 314
3.1 Sampling 315
3.2 Tight Sampling Spaces 315
3.3 Probability of Correct Selection 317
4 Implementation 318
4.1 Even Sampling 319
4.2 Adaptive Sampling 319
5 Experiments 320
5.1 Accuracy of Selection 320
5.2 Hypervolume-based Search 321
6 Conclusion 322
References 324
Using a Gradient Based Method to Seed an EMO Algorithm 326
1 Introduction 326
2 Related Work 327
3 Definitions and Basic Concepts 329
4 Gradient Based Method for Multi-Objective Optimization 329
4.1 Single-Objective Gradient Based Method 331
5 Hybridization and Preliminary Results 332
6 Conclusions 334
References 335
Nadir Point Estimation Using Evolutionary Approaches:Better Accuracy and Computational Speed ThroughFocused Search 337
1 Introduction 338
2 Nadir Point Estimation Procedures with Accuracyand Computational Time 339
2.1 Surface-to-Nadir: Computing Solutions from Entire Pareto-optimal Surface 340
2.2 Edge-to-Nadir: Computing Edge Solutionsof Pareto-optimal Surface 340
2.3 Extreme-point-to-Nadir: Computing Objective-wiseWorst Pareto-optimal Points 341
2.3.1 Extremized Crowded NSGA-II 341
2.3.2 Proposed Local Search Approach 341
2.3.3 Hybrid Extreme-point-to-nadir Algorithm 343
3 Results on Numerical Test Problems 344
3.1 Problem SZ1 344
3.2 Problem SZ2 347
3.3 Problem IS 348
3.4 Problem KM 350
4 Conclusions 351
References 352
A Branch and Bound Algorithm for Choquet Optimization in Multicriteria Problems 353
1 Introduction 353
2 Choquet Integral and Well-balanced Solutions 354
3 Determination of Choquet-optimal Solutions 357
4 Applications 360
5 Conclusion 362
References 362
Decision Space Diversity Can Be Essential for Solving Multiobjective Real-World Problems 364
1 Introduction 364
2 Aims and Methods 365
3 Real-world Distillation Plant Layout Problem 366
4 First Assessment of the Problem Structure 367
5 Tuning via Constructive Modeling 369
6 Conclusions 373
References 373
Computing and Selecting -Efficient Solutionsof 0,1-Knapsack Problems 375
1 Introduction 375
2 Background 376
3 The Problem 377
4 A Stochastic Search Algorithm 379
4.1 The Algorithm 379
4.2 Discussion and Analysis 380
4.3 Numerical Results 382
5 Interactive Selection Method 383
6 Conclusions 384
References 384