Optimization, Control, and Applications in the Information Age: In Honor of Panos M. Pardalos’s 60th Birthday (Springer Proceedings in Mathematics & Statistics Book 130)

Recent developments in theory, algorithms, and applications in optimization and control are discussed in this proceedings, based on selected talks from the 'Optimization, Control, and Applications in the Information Age' conference, organized in honor of Panos Pardalos's 60th birthday. This volume contains numerous applications to optimal decision making in energy production and fuel management, data mining, logistics, supply chain management, market network analysis, risk analysis, and community network analysis. In addition, a short biography is included describing Dr. Pardalos's path from a shepherd village on the high mountains of Thessaly to academic success. Due to the wide range of topics such as global optimization, combinatorial optimization, game theory, stochastics and programming contained in this publication, scientists, researchers, and students in optimization, operations research, analytics, mathematics and computer science will be interested in this volume Preface......Page 10 Contents......Page 12 Contributors......Page 16 List of Participants......Page 20 Panos M. Pardalos: A Brief Biography......Page 24 1 Introduction......Page 27 2 What Is a Modular?......Page 28 3 Modular Spaces......Page 31 4 Modular Lipschitzian Maps......Page 33 5 Modular Contractions......Page 36 6 An Ad Hoc Application......Page 37 7 Conclusion......Page 40 References......Page 41 A Taxonomy for the Flexible Job Shop Scheduling Problem......Page 42 1 Introduction......Page 43 2 The FJSP......Page 44 3 A Brief Literature Review......Page 45 4 Statistical Findings......Page 50 5 FJSP Taxonomy......Page 52 6 Classification of the FJSP Literature......Page 57 7 Concluding Remarks......Page 58 References......Page 59 Sensitivity Analysis of Welfare, Equity, and Acceptability Level of Transport Policies......Page 63 1 Introduction......Page 64 2.2 The Car (Private Transportation) Network Model......Page 67 2.3 The Public Transport Model......Page 68 2.4 The Travel Demand Model......Page 69 2.5 The Optimization Problem......Page 70 3.1 Welfare Indicator Formulation......Page 71 3.2 Inequality Indicator Formulation......Page 72 3.2.1 The Generalised Entropy Class of Inequality Measures......Page 73 3.2.2 Theil's Entropy Measure......Page 74 3.3 Acceptability Indicator Formulation......Page 75 4.1 Application to the Present Problem......Page 78 5 Numerical Experiments......Page 82 5.2 Sensitivity Analysis Results......Page 83 6 Conclusions......Page 87 References......Page 88 1 Introduction......Page 90 2 Calibration as an Optimization Problem......Page 91 3 Choice of Functions φi in (1)......Page 93 4 Hard Calibration......Page 97 4.1 Example 1: A Classical Example......Page 98 4.2 Example 2......Page 102 4.3 Example 3......Page 104 4.4 Example 4......Page 105 5 Soft Calibration......Page 106 6 Conclusions......Page 110 References......Page 111 1 Introduction......Page 113 2 An Outline of the Method of Calculation......Page 115 3 Changes in the Solution Due to Changes in the Data......Page 117 4 An Illustrative Example......Page 121 5 The Simulation Experiment......Page 128 6 Discussion......Page 132 References......Page 133 Modeling and Solving Vehicle Routing Problems with Many Available Vehicle Types......Page 135 1 Introduction......Page 136 2 Literature Review of the VRP with a Heterogeneous Fleet......Page 137 3 Mathematical Models for the Many-FSMVRP......Page 138 3.1 A Set-Partitioning Formulation of the FSMVRP......Page 139 3.2 An Extended Set-Partitioning Model of the many-FSMVRP......Page 140 3.3 Load-dependent Costs......Page 141 4.1 Column Generation Applied to the Set-Partitioning Model......Page 142 4.1.1 Adding Routes to the Restricted Master Problem......Page 143 4.2 Benders' Decomposition Algorithm for the Extended Set-Partitioning Model......Page 145 4.2.1 An Optimal Extreme Point to the Benders Subproblem......Page 148 4.2.2 Extensions of Benders' Algorithm......Page 150 5 Tests and Results......Page 151 5.2 Comparison of the Algorithms......Page 152 5.3 Comparison of the Solutions Obtained Using Different Models......Page 155 6 Conclusions......Page 157 Appendix: The Extended Test Instances many-FSMVRP5......Page 158 References......Page 159 1 Introduction......Page 161 2 Problem Definition and Formulation......Page 163 3.2 Biased Random-Key Genetic Algorithm......Page 166 3.2.1 Chromosome Representation and Decoding......Page 168 3.2.2 Solution Builder......Page 169 4 Computational Experiments......Page 171 References......Page 172 1 Introduction......Page 175 2 Sequential Deterministic Facility Location Problems......Page 176 3 Sequential Probabilistic Competitive Facility Location Models......Page 182 4 Competitive Facility Location with Competition of Customers......Page 185 References......Page 190 1 Introduction......Page 192 2 Problem Formulation and Some Preliminary Results......Page 193 3 Nash Equilibria Conditions for Stochastic Positional Games with Average Payoffs......Page 195 4 Saddle Point Conditions for Antagonistic Stochastic Positional Games and an Algorithm for Determining the Optimal Strategies......Page 199 4.1 An Algorithm......Page 203 5 Application of Stochastic Positional Games for Studying Shapley Stochastic Games......Page 204 References......Page 206 1 Introduction......Page 208 3 Adaptive Multi-Swarm Particle Swarm Optimization Algorithm......Page 211 4 Computational Results......Page 215 5 Conclusions......Page 223 References......Page 226 1 Introduction......Page 229 2 Eigendecomposition of the Mean-Variance Model......Page 231 2.1 Approximation of the Mean-Variance Model......Page 232 3.1 A Linearized Error Term......Page 235 3.2 Cardinality of the Solution......Page 239 4 Numerical Illustrations......Page 240 4.2 Deviation in Solution......Page 241 4.3 Efficient Frontier......Page 244 4.4 Cardinality of the Solution......Page 246 5 A Proposed Transformation......Page 247 6 Conclusion and Further Research......Page 251 References......Page 252 Three Aspects of the Research Impact by a Scientist: Measurement Methods and an Empirical Evaluation......Page 253 1 Introduction: The Problem and Background......Page 254 2.1 The Problem of Stratification......Page 256 2.2 Linstrat Criterion and Method......Page 259 2.3 Taxonomic Rank of a Scientist......Page 260 3.1 A Taxonomy of the Data Analysis Subjects......Page 263 3.2 Sample of Scientists and Their Taxonomic Ranks......Page 264 3.3 Scoring Citation and Merit......Page 272 3.4 Combined Criteria and Stratifications Obtained......Page 273 4 Conclusion......Page 277 References......Page 278 1 Introduction......Page 280 3 Two-Norm Support Vector Classification......Page 282 4 Kernel Learning......Page 284 5.1 Uncertainty Mapping for Input to Feature Space......Page 285 5.2 Robust Counterparts to Uncertain Constraints......Page 287 6 Empirical Results......Page 288 References......Page 291 Multi-Objective Optimization and Multi-Attribute Decision Making for a Novel Batch Scheduling Problem Based on Mould Capabilities......Page 293 1 Introduction......Page 294 2.1.1 Batch Scheduling Problems with Single-Objective......Page 295 2.1.2 Batch Scheduling with Multi-Objective......Page 296 2.3 Summary......Page 297 3.2 Problem Description......Page 298 3.3 Mixed Integer Programming Model......Page 300 4 Properties of Optimal Solutions......Page 301 5.1 Gravitational Search Algorithm......Page 302 5.2.1 Encoding......Page 303 5.2.2 Decoding......Page 304 5.2.4 Mutation Operator......Page 305 6 Multi-Attribute Decision Making......Page 306 7.2 Results of MADM and the Schedule's Gantt Chart Corresponding to Optimal Solutions......Page 309 8 Conclusions......Page 310 References......Page 314 1 Introduction......Page 316 2 Problem Setting......Page 318 3 A Time-Indexed Mathematical Model......Page 321 4 Column Generation......Page 324 5 Stabilized Column Generation......Page 326 6 Bounding Properties......Page 327 7 Numerical Validation......Page 328 8 Conclusion......Page 329 References......Page 330 1 Problem Statement......Page 332 2 Diagonal Partition Strategies......Page 334 3 Diagonal Global Optimization Methods......Page 339 4 Results of Numerical Comparison......Page 345 References......Page 349 1 Introduction......Page 352 2 Finite Element Modeling of Piezoelectric Smart Structures......Page 354 2.1 State Space Formulation of the Modal Control Problem......Page 355 3 Optimal Controller Design......Page 356 3.1 Optimization of Actuator Location and Voltages in Shape Control......Page 357 3.3 Optimization Implementation Using Genetic Algorithms......Page 358 4 Numerical Applications......Page 359 4.1.1 A Cantilever Plate Subject to a Point Force at the Tip......Page 360 4.1.2 A Cantilever Plate Subject to a Uniform Load......Page 361 4.2 Optimal Actuator Locations in Vibration Control......Page 363 5 Conclusions......Page 364 References......Page 365 1 Introduction......Page 366 2.1 The ACC Algorithm......Page 367 3 Experimental Part......Page 369 4 Conclusions......Page 372 References......Page 374 1 Introduction......Page 375 2 The Contact Network......Page 377 2.1 Generating a Representative Population......Page 378 2.2 Generating Appropriate Contact Patterns......Page 379 2.3 Resulting Network Structure......Page 381 2.5 Calculating the Probability of a Pandemic......Page 382 3.2 Network Centrality......Page 383 4 Computational Results......Page 385 4.1 Decision Trees......Page 386 4.2 Mitigation by Dissemination Block......Page 387 4.3 Not Considering Dissemination Block......Page 388 4.4 Effect on the Potential Pandemic......Page 389 5 Discussion and Conclusions......Page 391 References......Page 395 1 Introduction......Page 398 2 Motivation of the Research......Page 399 3 Statement of the Problem......Page 401 4 Assessment of Approximation......Page 402 5 Conclusions......Page 405 References......Page 406 Advanced Statistical Tools for Modelling of Composition and Processing Parameters for Alloy Development......Page 407 1 Introduction......Page 408 2 Percentile and Mixed Percentile Regression......Page 409 3 Building CVN Distributions and Estimation of CVaR for Specimens......Page 414 4 Comparison of VaR, and CVaR Risk Measures in Screening Process......Page 417 5 Precision of Estimates of CVaR Derived From Small Samples......Page 420 6 Summary and Conclusions......Page 425 References......Page 426