|
Honorary Committee |
8 |
|
|
Scientific Committee |
9 |
|
|
Organizing Committee |
11 |
|
|
Contents |
12 |
|
|
Part IDecision Making |
16 |
|
|
1 New Aggregation Methods for Decision-Making in the Selection of Business Opportunities |
17 |
|
|
Abstract |
17 |
|
|
1 Introduction |
18 |
|
|
2 Preliminares |
19 |
|
|
2.1 The OWA Operator |
19 |
|
|
2.2 The OWAAC Operator |
20 |
|
|
2.3 The OWAD Operator |
20 |
|
|
2.4 The OWAIMAM Operator |
20 |
|
|
2.5 A New Method for Dealing with the Weight of the OWA Operator |
21 |
|
|
3 Decision Making Approach for Starting New Business |
23 |
|
|
4 Numerical Example |
24 |
|
|
5 Conclusion |
30 |
|
|
References |
30 |
|
|
2 Credit Analysis Using a Combination of Fuzzy Robust PCA and a Classification Algorithm |
33 |
|
|
Abstract |
33 |
|
|
1 Introduction |
33 |
|
|
2 Classification Procedure |
35 |
|
|
2.1 Fuzzy Robust Principal Component Analysis (FRPCA) |
35 |
|
|
2.2 Fuzzy k-Nearest Neighbor Classifier |
36 |
|
|
2.3 Similarity Classifier |
37 |
|
|
3 Classification Results with an Australian Credit Screening Dataset |
38 |
|
|
3.1 Results from the Australian Credit Scoring Data with FRPCA and the Similarity Classifier |
38 |
|
|
3.2 Results from the Australian Credit Scoring Data with FRPCA and Fuzzy k-Nearest Neighbor Classifier |
40 |
|
|
3.3 Short Discussion About the Results |
41 |
|
|
4 Summary and Conclusions |
42 |
|
|
References |
43 |
|
|
3 Fuzzy TOPSIS for an Integrative Sustainability Performance Assessment: A Proposal for Wearing Apparel Industry |
44 |
|
|
Abstract |
44 |
|
|
1 Introduction |
45 |
|
|
2 Fuzzy Multi-criteria Decision-Making Method (MCDM) |
45 |
|
|
2.1 Fuzzy Inference System |
45 |
|
|
2.2 Fuzzy TOPSIS |
47 |
|
|
3 Empirical Design |
48 |
|
|
4 Results |
49 |
|
|
5 Conclusions |
51 |
|
|
References |
51 |
|
|
4 On the Orness of SUOWA Operators |
53 |
|
|
Abstract |
53 |
|
|
1 Introduction |
53 |
|
|
2 Preliminaries |
54 |
|
|
3 Choquet Integral |
55 |
|
|
3.1 Weighted Means and OWA Operators |
57 |
|
|
3.2 SUOWA Operators |
57 |
|
|
4 Orness Measures |
58 |
|
|
5 Conclusion |
62 |
|
|
Acknowledgments |
62 |
|
|
References |
62 |
|
|
5 OWA Operators in Portfolio Selection |
64 |
|
|
Abstract |
64 |
|
|
1 Introduction |
64 |
|
|
2 Preliminares |
66 |
|
|
2.1 The OWA Operator |
66 |
|
|
2.2 Portfolio Selection with Markowitz Approach |
67 |
|
|
3 The OWA Operator in Portfolio Selection |
69 |
|
|
3.1 Asset Return and Risk Using OWA |
69 |
|
|
3.2 Portfolio Mean Return and Risk Using OWA |
70 |
|
|
3.3 Investor's Criteria for Mean-OWA |
72 |
|
|
4 Conclusions |
73 |
|
|
References |
74 |
|
|
Part IIExpert Systems and Forgotten EffectsTheory |
76 |
|
|
6 Application of the Forgotten Effects Model to the Agency Theory |
77 |
|
|
Abstract |
77 |
|
|
1 Introduction |
77 |
|
|
2 The Recuperation of Forgotten Effects in the Resolution of the Agency Problem |
79 |
|
|
2.1 The Main Agency Problems (Effects) |
79 |
|
|
2.2 Mechanisms to Solve the Agency Problems (Causes) |
80 |
|
|
2.3 Process Work Out for the Detection of Possible Forgotten Effects |
81 |
|
|
3 Results |
87 |
|
|
4 Conclusions |
88 |
|
|
References |
89 |
|
|
7 Determining the Influence Variables in the Pork Price, Based on Expert Systems |
90 |
|
|
Abstract |
90 |
|
|
1 Introduction |
90 |
|
|
2 The Experts |
91 |
|
|
3 The Questionnaire |
92 |
|
|
4 The Variables |
92 |
|
|
5 The Process of Valuation from Experts and Making Expertons |
93 |
|
|
6 Analyzing Results |
97 |
|
|
7 Conclusions |
99 |
|
|
References |
101 |
|
|
8 Forgotten Effects Analysis Between the Regional Economic Activity of Michoacan and Welfare of Its Inhabitants |
102 |
|
|
Abstract |
102 |
|
|
1 Introduction |
102 |
|
|
2 Theoretical Background |
103 |
|
|
3 Methodology |
105 |
|
|
4 Application of the Model |
108 |
|
|
5 Results and Discussion |
112 |
|
|
6 Conclusions |
114 |
|
|
References |
114 |
|
|
9 Interval Numbers Versus Correlational Approaches Analyzing Corporate Social Responsibility |
115 |
|
|
Abstract |
115 |
|
|
1 Introduction |
116 |
|
|
2 Theoretical Background: CSR and Effectiveness |
117 |
|
|
3 OWA Operators: UPA, UWA and UPWA |
118 |
|
|
4 Procedure and Sample |
118 |
|
|
5 Results and Conclusions |
120 |
|
|
References |
121 |
|
|
10 Second-Order Changes on Personnel Assignment Under Uncertainty |
122 |
|
|
Abstract |
122 |
|
|
1 Introduction |
122 |
|
|
2 Methodology |
123 |
|
|
2.1 Employees Commitment Level Evaluation |
123 |
|
|
2.2 Fuzzy SOC |
124 |
|
|
3 Study Case |
129 |
|
|
4 Conclusions |
133 |
|
|
References |
133 |
|
|
Part IIIForecasting Models |
134 |
|
|
11 Advanced Spectral Methods and Their Potential in Forecasting Fuzzy-Valued and Multivariate Financial Time Series |
135 |
|
|
Abstract |
135 |
|
|
1 Introduction |
136 |
|
|
2 Approaches to Extracting Independent Factors from Signal Mixtures: The FastICA Algorithm |
136 |
|
|
3 Singular Spectrum Analysis, Its Multivariate Extension, and the Recurrent SSA Forecasting Procedure |
138 |
|
|
4 Applying MSSA to Forecasting Triangular-Shaped Fuzzy Monthly Exchange Rates |
141 |
|
|
5 A Hybrid ICA-SSA Approach to Separating and Forecasting Independent Factors and then Remixing the Forecasts into the Observable Foreign Exchange Rates |
142 |
|
|
6 Conclusion |
146 |
|
|
12 Goodness of Aggregation Operators in a Diagnostic Fuzzy Model of Business Failure |
147 |
|
|
Abstract |
147 |
|
|
1 Introduction |
148 |
|
|
2 The Diagnosis Model |
148 |
|
|
3 Limitations of the Model |
150 |
|
|
4 Grouping the Causes |
150 |
|
|
4.1 The Aggregation Operators |
151 |
|
|
4.2 Motion for Monitoring and Grouping of Causes |
152 |
|
|
5 Goodness Index |
153 |
|
|
6 Estimation of the Model |
154 |
|
|
7 Empirical Verification of the Grouping |
156 |
|
|
8 Conclusions |
157 |
|
|
Appendix A |
159 |
|
|
Appendix B |
160 |
|
|
References |
162 |
|
|
13 Forecasting Global Growth in an Uncertain Environment |
164 |
|
|
Abstract |
164 |
|
|
1 Introduction |
164 |
|
|
2 Literature Review |
166 |
|
|
3 Fuzzy Regression Model |
167 |
|
|
4 Forecasting Model of GDP Growth Rate |
168 |
|
|
5 Conclusion |
170 |
|
|
References |
170 |
|
|
14 Fuzzy NN Time Series Forecasting |
172 |
|
|
Abstract |
172 |
|
|
1 Introduction |
173 |
|
|
2 Nearest Neighbour Forecasting Method |
175 |
|
|
2.1 One-Step-Ahead Forecasting |
175 |
|
|
3 Fuzzy Logic |
176 |
|
|
3.1 Classical and Fuzzy Sets |
176 |
|
|
3.2 Fuzzy Operations |
176 |
|
|
4 Fuzzy Nearest Neighbours |
178 |
|
|
4.1 Fuzzy Linguistic Terms |
178 |
|
|
4.2 Fuzzy Rules |
178 |
|
|
4.3 Learning Phase |
179 |
|
|
4.4 Fuzzy Forecasting |
181 |
|
|
4.5 Multi-step Simultaneous Forecasting |
181 |
|
|
5 Study Case |
182 |
|
|
6 Conclusions |
183 |
|
|
References |
183 |
|
|
Part IVFuzzy Logic and Fuzzy Sets |
185 |
|
|
15 A Methodology for the Valuation of Quality Management System in a Fuzzy Environment |
186 |
|
|
Abstract |
186 |
|
|
1 Introduction |
186 |
|
|
2 Proposed Methodology |
187 |
|
|
2.1 Valuation of the Company with the ISO 9001 Quality System |
187 |
|
|
2.2 Valuation of the Company Without the ISO 9001 Quality System |
189 |
|
|
2.3 Valuation of the Quality System |
190 |
|
|
3 Case Study |
190 |
|
|
3.1 Valuation Assuming that It Has the Quality System Implementation |
192 |
|
|
3.2 Valuation Assuming that It Has not the Quality System Implemented |
193 |
|
|
3.3 Valuation of the ISO 9001 Quality System |
194 |
|
|
4 Conclusions |
195 |
|
|
References |
195 |
|
|
16 A Qualitative Study to Strong Allee Effect with Fuzzy Parameters |
197 |
|
|
Abstract |
197 |
|
|
1 Introduction |
197 |
|
|
2 Allee Effect Equation with Fuzzy Parameters |
199 |
|
|
3 Examples |
202 |
|
|
4 Conclusions |
206 |
|
|
References |
206 |
|
|
17 Distribution of Financial Resources Using a Fuzzy Transportation Model |
207 |
|
|
Abstract |
207 |
|
|
1 Introduction |
207 |
|
|
2 Preliminaries |
208 |
|
|
2.1 Classic Transportation Problem |
208 |
|
|
2.2 Elements of Fuzzy Sets |
209 |
|
|
2.3 Ranking Fuzzy Numbers |
210 |
|
|
3 Fuzzy Transportation Problem (FTP) |
210 |
|
|
4 Determining the Optimal Structure in the Financing of a Firm |
213 |
|
|
4.1 Application Case |
213 |
|
|
5 Final Comments |
218 |
|
|
References |
218 |
|
|
18 Clustering Variables Based on Fuzzy Equivalence Relations |
220 |
|
|
Abstract |
220 |
|
|
1 Introduction |
220 |
|
|
2 Literature Review |
222 |
|
|
3 Fuzzy Equivalence Relations |
223 |
|
|
4 Proposed Method for Clustering Variables and Factor Analysis Based on Fuzzy Equivalence Relation |
224 |
|
|
4.1 Empirical Example -- Variable Clustering |
225 |
|
|
4.2 Empirical Example -- Factor Analysis |
228 |
|
|
5 Some Interpretations of the Clustered Variables |
230 |
|
|
6 Conclusions |
230 |
|
|
Acknowledgments |
230 |
|
|
References |
231 |
|
|
19 Fuzzy EOQ Inventory Model With and Without Production as an Enterprise Improvement Strategy |
232 |
|
|
Abstract |
232 |
|
|
1 Introduction |
232 |
|
|
2 Stock Costs |
234 |
|
|
3 Demand Behavior |
234 |
|
|
4 Classical Economic Order(ing) Quantity (EOQ) |
235 |
|
|
5 Classical Economic Order(ing) Quantity (EOQ) with Production |
237 |
|
|
6 Case Analysis |
240 |
|
|
7 Results |
240 |
|
|
8 Conclusions |
241 |
|
|
9 Recommendations |
242 |
|
|
References |
242 |
|
|
Part VModelling and Simulation Techniques |
243 |
|
|
20 A Bibliometric Overview of Financial Studies |
244 |
|
|
Abstract |
244 |
|
|
1 Introduction |
244 |
|
|
2 Citation Structure in Finance |
245 |
|
|
3 Journal Rankings |
247 |
|
|
4 The Most Influential Papers in Finance |
249 |
|
|
5 Conclusions |
253 |
|
|
Acknowledgments |
253 |
|
|
References |
253 |
|
|
21 A Theoretical Approach to Endogenous Development Traps in an Evolutionary Economic System |
254 |
|
|
Abstract |
254 |
|
|
1 Introduction |
254 |
|
|
2 ESO Systems |
257 |
|
|
3 Economic Systems in Evolution |
259 |
|
|
4 Economics ESO Systems |
262 |
|
|
4.1 Development Traps and Unsuccessful EESO Systems |
264 |
|
|
5 Discussion |
265 |
|
|
References |
266 |
|
|
22 ABC, A Viable Algorithm for the Political Districting Problem |
267 |
|
|
Abstract |
267 |
|
|
1 Introduction |
268 |
|
|
2 Problem Description |
269 |
|
|
3 Heuristic Algorithms |
270 |
|
|
3.1 Simulated Annealing |
270 |
|
|
3.2 Simulated Annealing Adaptation |
271 |
|
|
3.3 Artificial Bee Colony |
271 |
|
|
3.4 Artificial Bee Colony Adaptation |
272 |
|
|
4 Computational Experiments |
273 |
|
|
5 Conclusions |
275 |
|
|
References |
275 |
|
|
23 Asymmetric Uncertainty of Mortality and Longevity in the Spanish Population |
277 |
|
|
Abstract |
277 |
|
|
1 Introduction |
278 |
|
|
2 Methodology |
279 |
|
|
2.1 Factor Models |
279 |
|
|
3 Data and Results |
281 |
|
|
4 Conclusions |
284 |
|
|
24 Joint Modeling of Health Care Usage and Longevity Uncertainty for an Insurance Portfolio |
286 |
|
|
Abstract |
286 |
|
|
1 Introduction |
286 |
|
|
2 Data and Methods |
288 |
|
|
2.1 Longitudinal Submodel: Random Intercept Model |
289 |
|
|
2.2 Survival Submodel: PH Cox Model |
289 |
|
|
2.3 Joint Model for Longitudinal and Survival Data |
290 |
|
|
3 Results and Prediction |
291 |
|
|
4 Conclusions |
292 |
|
|
References |
293 |
|
|
25 The Commodities Financialization As a New Source of Uncertainty: The Case of the Incidence of the Interest Rate Over the Maize Price During 1990--2014 |
295 |
|
|
Abstract |
295 |
|
|
1 Introduction |
295 |
|
|
2 Commodities Financialization |
296 |
|
|
3 The Interest Rate Incidence Over the Maize Price |
297 |
|
|
3.1 VAR Model |
298 |
|
|
3.2 Testing and Results |
299 |
|
|
3.3 Period 1990--2003 |
299 |
|
|
3.4 Period 2004--2014 |
301 |
|
|
4 Conclusions |
303 |
|
|
26 The Fairness/Efficiency Issue Explored Through El Farol Bar Model |
304 |
|
|
Abstract |
304 |
|
|
1 Introduction |
304 |
|
|
2 Efficiency of Social Choices According to the El Farol Bar Model |
306 |
|
|
3 NetLogo Implementations of El Farol Bar Model |
307 |
|
|
3.1 The Original El Farol Bar Problem (AG9404-Original) |
309 |
|
|
3.1.1 Predictors |
309 |
|
|
3.1.2 Learning System |
309 |
|
|
3.2 El Farol with Weighted Predictors (FW9907-Weighted) |
310 |
|
|
3.2.1 Predictors |
310 |
|
|
3.2.2 Learning System |
311 |
|
|
3.3 El Farol with a Cyclic Strategy (BSW9903-Cyclic) |
311 |
|
|
3.3.1 Predictors |
311 |
|
|
3.3.2 Learning System |
311 |
|
|
3.4 El Farol with a Random Choice (FS9914-Random) |
312 |
|
|
3.4.1 Predictors |
312 |
|
|
3.4.2 Learning System |
312 |
|
|
3.5 Discussion |
312 |
|
|
4 The Fairness Issue |
313 |
|
|
4.1 A Measurement of Iniquity: The Gini Coefficient |
313 |
|
|
4.2 More Accurate Measurements of Efficiency and Fairness |
315 |
|
|
4.2.1 Fair Quantity (FQ) and Fair Period (FP) |
315 |
|
|
4.2.2 Measurement of Efficiency in the Fair Period |
316 |
|
|
4.2.3 Measurement of Fairness in the Fair Period |
316 |
|
|
4.2.4 Application of New Measurements of Efficiency and Fairness |
317 |
|
|
4.3 A Time-Box View of Fairness |
317 |
|
|
5 Conclusions and Guidelines for Future Research |
320 |
|
|
6 NetLogo Models |
321 |
|
|
References |
322 |
|
|
Part VINeural Networks and Genetic Algorithms |
323 |
|
|
27 Comparative Analysis Between Sustainable Index and Non-sustainable Index with Genetic Algorithms: Application to OECD Countries |
324 |
|
|
Abstract |
324 |
|
|
1 Introduction |
324 |
|
|
2 RSC by Regions |
325 |
|
|
3 Literature Review |
326 |
|
|
4 Theoretical Framework |
327 |
|
|
5 Genetic Algorithm |
329 |
|
|
6 Sample |
330 |
|
|
7 Software |
332 |
|
|
8 Results |
332 |
|
|
9 Conclusions |
337 |
|
|
References |
338 |
|
|
28 Sovereign Bond Spreads and Economic Variables of European Countries Under the Analysis of Self-organizing Maps |
340 |
|
|
Abstract |
340 |
|
|
1 Introduction |
341 |
|
|
2 Literature Review |
342 |
|
|
3 Methodology and Data |
343 |
|
|
4 Results |
344 |
|
|
5 Conclusions |
349 |
|
|
References |
350 |
|
|
29 Using Genetic Algorithms to Evolve a Type-2 Fuzzy Logic System for Predicting Bankruptcy |
352 |
|
|
Abstract |
352 |
|
|
1 Introduction |
352 |
|
|
2 Type-2 Fuzzy Sets |
353 |
|
|
3 Interval Type-2 Fuzzy Logic Systems |
354 |
|
|
3.1 Main Components |
354 |
|
|
3.2 Fuzzifier |
356 |
|
|
3.3 Fuzzy Inference Engine |
356 |
|
|
3.4 Type-Reducer and Defuzzifier |
357 |
|
|
4 Evolving an IT2FLS by Genetic Algorithms for Predicting Bankruptcy |
358 |
|
|
5 Conclusion |
361 |
|
|
References |
362 |
|
|
Part VIIOptimization and Control |
363 |
|
|
30 Hedge for Automotive SMEs Using An Exotic Option |
364 |
|
|
Abstract |
364 |
|
|
1 Introduction |
364 |
|
|
2 Stochastic Model |
365 |
|
|
3 A Hedging Tool |
368 |
|
|
3.1 Ford Motor Company |
368 |
|
|
3.2 Renault |
369 |
|
|
3.3 Peugeot |
370 |
|
|
4 Conclusion |
371 |
|
|
References |
372 |
|
|
31 Obtaining Classification Rules Using LVQ+PSO: An Application to Credit Risk |
373 |
|
|
Abstract |
373 |
|
|
1 Introduction |
374 |
|
|
2 Related Work |
374 |
|
|
3 Learning Vector Quantization (LVQ) |
375 |
|
|
4 Obtaining Classification Rules with Particle Swarm Optimization (PSO) |
376 |
|
|
5 LVQ+PSO. Proposed Method for Obtaining Rules |
377 |
|
|
6 Results |
379 |
|
|
7 Conclusions |
380 |
|
|
Acknowledgments |
380 |
|
|
References |
380 |
|
|
32 Optimization of Securitized Cash Flows for Toll Roads |
382 |
|
|
Abstract |
382 |
|
|
1 Introduction |
383 |
|
|
2 The Selection of the Portfolio of Securitized Cash Flows |
384 |
|
|
2.1 Defining the Objective Functions |
384 |
|
|
2.2 Adding the Constraint Functions |
389 |
|
|
3 Solving the Optimization Problem by Fuzzy Programming |
390 |
|
|
4 Conclusions |
393 |
|
|
References |
394 |
|
|
33 SC: A Fuzzy Approximation for Nonlinear Regression Optimization |
395 |
|
|
Abstract |
395 |
|
|
1 Introduction |
396 |
|
|
2 Nonlinear Regression |
397 |
|
|
3 Fuzzy Numbers |
398 |
|
|
4 SC: System of Convergence |
399 |
|
|
5 Particle Swarm Optimization |
400 |
|
|
6 SC-PSO-3P |
401 |
|
|
7 Computational Results |
402 |
|
|
8 Conclusions and Further Research |
406 |
|
|
Acknowledgments |
406 |
|
|
References |
406 |
|
|
34 Winding Indexes at Specific Traveling Salesman Problems |
408 |
|
|
Abstract |
408 |
|
|
1 Introduction |
408 |
|
|
2 Recapitulation of Concepts |
409 |
|
|
2.1 Natural Evaluation of Euclidean Path Lengths |
411 |
|
|
2.2 Resuming Terms and Definitions |
411 |
|
|
3 Statements for the Winding Indexes |
413 |
|
|
3.1 Winding Indexes at the Euclidean Quasi-Hamiltonian Cycles in N-Gons |
418 |
|
|
4 Bistarred Hamiltonian Cycles in Coupled N-Gons |
422 |
|
|
5 Existence of Bistarred Hamiltonian Polygonals in Coupled Nodd-Gons |
423 |
|
|
5.1 Existence of Bistarred Quasi-Hamiltonian Cycles in Coupled Neven-Gons |
426 |
|
|
6 Conclusion |
427 |
|
|
References |
427 |
|
|
Author Index |
429 |
|