Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices

[EN] After the recent establishment of the Sustainable Development Goals and the Agenda 2030, the sustainable design of products in general and infrastructures in particular emerge as a challenging field for the development and application of multicriteria decision-making tools. Sustainability-related decision problems usually involve, by definition, a wide variety in number and nature of conflicting criteria, thus pushing the limits of conventional multicriteria decision-making tools practices. The greater the number of criteria and the more complex the relations existing between them in a decisional problem, the less accurate and certain are the judgments required by usual methods, such as the analytic hierarchy process (AHP). The present paper proposes a neutrosophic AHP completion methodology to reduce the number of judgments required to be emitted by the decision maker. This increases the consistency of their responses, while accounting for uncertainties associated to the fuzziness of human thinking. The method is applied to a sustainable-design problem, resulting in weight estimations that allow for a reduction of up to 22% of the conventionally required comparisons, with an average accuracy below 10% between estimates and the weights resulting from a conventionally completed AHP matrix, and a root mean standard error below 15%.The authors acknowledge the financial support of the Spanish Ministry of Economy and Business, along with FEDER funding (DIMALIFE Project: BIA2017-85098-R).Navarro, IJ.; Martí Albiñana, JV.; Yepes, V. (2021). Neutrosophic Completion Technique for Incomplete Higher-Order AHP Comparison Matrices. Mathematics. 9(5):1-19. https://doi.org/10.3390/math905049611995Worrell, E., Price, L., Martin, N., Hendriks, C., & Meida, L. O. (2001). CARBON DIOXIDE EMISSIONS FROM THE GLOBAL CEMENT INDUSTRY. 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Structure and Infrastructure Engineering, 16(7), 949-967. doi:10.1080/15732479.2019.1676791Tavana, M., Shaabani, A., Javier Santos-Arteaga, F., & Raeesi Vanani, I. (2020). A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics. Energies, 13(15), 3947. doi:10.3390/en13153947Yannis, G., Kopsacheili, A., Dragomanovits, A., & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 413-431. doi:10.1016/j.jtte.2020.05.005Navarro, I. J., Penadés-Plà, V., Martínez-Muñoz, D., Rempling, R., & Yepes, V. (2020). LIFE CYCLE SUSTAINABILITY ASSESSMENT FOR MULTI-CRITERIA DECISION MAKING IN BRIDGE DESIGN: A REVIEW. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 26(7), 690-704. doi:10.3846/jcem.2020.13599Hedelin, B. (2018). Complexity is no excuse. Sustainability Science, 14(3), 733-749. doi:10.1007/s11625-018-0635-5Zadeh, L. A. (1973). 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The neutrosophic number generalized weighted power averaging operator and its application in multiple attribute group decision making. International Journal of Machine Learning and Cybernetics, 9(2), 347-358. doi:10.1007/s13042-016-0508-0Peng, J., Wang, J., & Yang, W.-E. (2016). A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems. International Journal of Systems Science, 48(2), 425-435. doi:10.1080/00207721.2016.1218975Saaty, T. L., & Ozdemir, M. S. (2003). Why the magic number seven plus or minus two. Mathematical and Computer Modelling, 38(3-4), 233-244. doi:10.1016/s0895-7177(03)90083-5Harker, P. T. (1987). Incomplete pairwise comparisons in the analytic hierarchy process. Mathematical Modelling, 9(11), 837-848. doi:10.1016/0270-0255(87)90503-3Chen, K., Kou, G., Michael Tarn, J., & Song, Y. (2015). Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices. 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A Comparison between Fixed-Basis and Variable-Basis Schemes for Function Approximation and Functional Optimization

Fixed-basis and variable-basis approximation schemes are compared for the problems of function approximation and functional optimization (also known as infinite programming). Classes of problems are investigated for which variable-basis schemes with sigmoidal computational units perform better than fixed-basis ones, in terms of the minimum number of computational units needed to achieve a desired error in function approximation or approximate optimization. Previously known bounds on the accuracy are extended, with better rates, to families o

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

[EN] The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056.García, J.; Astorga, G.; Yepes, V. (2021). An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics. Mathematics. 9(3):1-20. https://doi.org/10.3390/math9030225S12093Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574Guo, H., Liu, B., Cai, D., & Lu, T. (2016). Predicting protein–protein interaction sites using modified support vector machine. International Journal of Machine Learning and Cybernetics, 9(3), 393-398. doi:10.1007/s13042-015-0450-6Korkmaz, S., Babalik, A., & Kiran, M. S. (2017). An artificial algae algorithm for solving binary optimization problems. International Journal of Machine Learning and Cybernetics, 9(7), 1233-1247. doi:10.1007/s13042-017-0772-7García, J., Martí, J. V., & Yepes, V. (2020). The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm. Mathematics, 8(6), 862. doi:10.3390/math8060862Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Talbi, E.-G. (2015). Combining metaheuristics with mathematical programming, constraint programming and machine learning. Annals of Operations Research, 240(1), 171-215. doi:10.1007/s10479-015-2034-yJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62-72. doi:10.1016/j.orp.2015.03.001Chou, J.-S., & Nguyen, T.-K. (2018). Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142. doi:10.1109/tii.2018.2794389Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Systems with Applications, 42(20), 7110-7120. doi:10.1016/j.eswa.2015.04.066De León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Marcos Moreno-Vega, J. (2017). A Machine Learning-based system for berth scheduling at bulk terminals. Expert Systems with Applications, 87, 170-182. doi:10.1016/j.eswa.2017.06.010García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006García, J., Crawford, B., Soto, R., Castro, C., & Paredes, F. (2017). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. doi:10.1007/s10489-017-0972-6Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). 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Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, 185-204. doi:10.1016/j.knosys.2018.08.003Feng, Y., An, H., & Gao, X. (2018). The Importance of Transfer Function in Solving Set-Union Knapsack Problem Based on Discrete Moth Search Algorithm. Mathematics, 7(1), 17. doi:10.3390/math7010017Zhang, G. (2010). Quantum-inspired evolutionary algorithms: a survey and empirical study. Journal of Heuristics, 17(3), 303-351. doi:10.1007/s10732-010-9136-0Srikanth, K., Panwar, L. K., Panigrahi, B., Herrera-Viedma, E., Sangaiah, A. K., & Wang, G.-G. (2018). Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. Computers & Electrical Engineering, 70, 243-260. doi:10.1016/j.compeleceng.2017.07.023Hu, H., Yang, K., Liu, L., Su, L., & Yang, Z. (2019). Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm. Water Resources Management, 33(7), 2357-2379. doi:10.1007/s11269-018-2138-7Gao, Y. J., Zhang, F. M., Zhao, Y., & Li, C. (2019). A novel quantum-inspired binary wolf pack algorithm for difficult knapsack problem. International Journal of Wireless and Mobile Computing, 16(3), 222. doi:10.1504/ijwmc.2019.099861Kumar, Y., Verma, S. K., & Sharma, S. (2020). Quantum-inspired binary gravitational search algorithm to recognize the facial expressions. International Journal of Modern Physics C, 31(10), 2050138. doi:10.1142/s0129183120501387Balas, E., & Padberg, M. W. (1976). Set Partitioning: A survey. SIAM Review, 18(4), 710-760. doi:10.1137/1018115Borneman, J., Chrobak, M., Della Vedova, G., Figueroa, A., & Jiang, T. (2001). Probe selection algorithms with applications in the analysis of microbial communities. Bioinformatics, 17(Suppl 1), S39-S48. doi:10.1093/bioinformatics/17.suppl_1.s39Boros, E., Hammer, P. L., Ibaraki, T., & Kogan, A. (1997). Logical analysis of numerical data. 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Tests of fit for the logarithmic distribution

Smooth tests for the logarithmic distribution are compared with three tests: the first is a test due to Epps and is based on a probability generating function, the second is the Anderson-Darling test, and the third is due to Klar and is based on the empirical integrated distribution function. These tests all have substantially better power than the traditional Pearson-Fisher X2 test of fit for the logarithmic. These traditional chi-squared tests are the only logarithmic tests of fit commonly applied by ecologists and other scientists

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008