Powiadomienia systemowe
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
- Sesja wygasła!
Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper presents a new aggregation operator tech‐ nique that uses the ordered weighted average (OWA), heavy aggregation operators, Hamming distance, and moving averages. This approach is called heavy ordered weighted moving average distance (HOWMAD). The main advantage of this operator is that it can use the characteristics of the HOWMA operator to under‐ or over‐ estimate the results according to the expectations and the knowledge of the future scenarios, analyze the his‐ torical data of the moving average, and compare the different alternatives with the ideal results of the dis‐ tance measures. Some of the main families and specific cases using generalized and quasi‐arithmetic means are presented, such as the generalized heavy moving aver‐ age distance and a generalized HOWMAD. This study develops an application of this operator in forecasting the sales growth rate for a commercial company. We find that it is possible to determine whether the company’s objectives can be achieved or must be reevaluated in response to the actual situation and future expectations of the enterprise.
Rocznik
Tom
Strony
18--27
Opis fizyczny
Bibliogr. 49 poz., rys.
Twórcy
autor
- Unidad Regional Culiacán, Universidad Autónoma de Occidente, Culiacán, Sinaloa, México
autor
- Tecnológico Nacional de México/Instituto Tecnológico de Culiacán, Sinaloa, México
autor
- Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepción, Chile
autor
- Facultad de Psicología, Universidad Autónoma de Sinaloa, Culiacán, Sinaloa, Mexico
autor
- Facultad de Ciencias Económicas y Administrativas, Escuela de Administración de Empresas, Universidad Pedagógica y Tecnológica de Colombia, Av. Central del Norte, 39‐115, 150001, Tunja, Colombia
Bibliografia
- [1] J. Gil‐Aluja. Elements for a theory of decision in uncertainty, vol. 32, 1999, Springer Science & Business Media.
- [2] S. Siddique, U. Ahmad, and M. Akram. “A Decision‐Making Analysis with Generalized m‐Polar Fuzzy Graphs,” Journal of Multiple-Valued Logic & Soft Computing, 37, 2021, 533–552.
- [3] R. W. Hamming. “Error detecting and error correcting codes,” The Bell system technical journal, 29(2), 1950, 147–160.
- [4] J. M. Merigó, and M. Casanovas. “Induced aggregation operators in the Euclidean distance and its application in financial decision making,” Expert systems with applications, 38(6), 2011, 7603–7608.
- [5] H. Xu, W. Zeng, X. Zeng, and G. G. Yen. “An evolutionary algorithm based on Minkowski distance for many‐objective optimization,” IEEE transactions on cybernetics, 49(11), 2018, 3968–3979.
- [6] E. Szmidt, and J. Kacprzyk. “Distances between intuitionistic fuzzy sets,” Fuzzy sets and systems, 114(3), 2000, 505–518.
- [7] J. Bae, L. Liu, J. Caverlee, and W. B. Rouse. Process mining, discovery, and integration using distance measures, 2006, 479–488.
- [8] A. Kaufmann, J. G. Aluja, and J. M. F. Pirla. Introducción de la teoría de los subconjuntos borrosos a la gestión de las empresas, 1986, Milladoiro.
- [9] V. G. Alfaro‐Garcia, J. M. Merigó, A. M. Gil‐Lafuente, and J. Kacprzyk. “Logarithmic aggregation operators and distance measures,” International Journal of Intelligent Systems, 33(7), 2018, 1488–1506.
- [10] E. Avilés‐Ochoa, E. León‐Castro, L. A. Perez‐Arellano, and J. M. Merigó. “Government transparency measurement through prioritized distance operators,” Journal of Intelligent & Fuzzy Systems, 34(4), 2018, 2783–2794.
- [11] F. Blanco‐Mesa, J. M. Merigó, and J. Kacprzyk. “Bonferroni means with distance measures and the adequacy coefficient in entrepreneurial group theory,” Knowledge-Based Systems, 111, 2016, 217–227.
- [12] L. A. Perez‐Arellano, F. Blanco‐Mesa, E. Leon‐Castro, and V. Alfaro‐Garcia. “Bonferroni prioritized aggregation operators applied to government transparency,” Mathematics, 9(1), 2021, 24.
- [13] W. Su, S. Zeng, and X. Ye. “Uncertain group decision‐making with induced aggregation operators and Euclidean distance,” Technological and Economic Development of Economy, 19(3), 2013, 431–447.
- [14] G. Selvachandran, P. K. Maji, R. Q. Faisal, and A. Razak Salleh. “Distance and distance induced intuitionistic entropy of generalized intuitionistic fuzzy soft sets,” Applied Intelligence, 47(1), 2017, 132–147.
- [15] J. M. Merigó, and M. Casanovas. “Induced aggregation operators in the Euclidean distance and its application in financial decision making,” Expert systems with applications, 38(6), 2011, 7603–7608.
- [16] B. Juang, and L. R. Rabiner. “A probabilistic distance measure for hidden Markov models,” AT&T technical journal, 64(2), 1985, 391–408.
- [17] R. Şahin, and G. D. Küçük. “Group decision making with simplified neutrosophic ordered weighted distance operator,” Mathematical Methods in the Applied Sciences, 41(12), 2018, 4795–4809.
- [18] D.‐H. Peng, T.‐D. Wang, C.‐Y. Gao, and H. Wang. “Enhancing relative ratio method for MCDM via attitudinal distance measures of intervalvalued hesitant fuzzy sets,” International Journal of Machine Learning and Cybernetics, 8(4), 2017, 1347–1368.
- [19] S. Xian, and W. Sun. “Fuzzy linguistic induced Euclidean OWA distance operator and its application in group linguistic decision making,”International Journal of Intelligent Systems, 29(5), 2014, 478–491.
- [20] R. R. Yager. “On ordered weighted averaging aggregation operators in multicriteria decisionaking,” IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 1988, 183–190.
- [21] J. Kacprzyk, R. R. Yager, and J. M. Merigo. “Towards human‐centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on Zadeh’s inspirations,” IEEE Computational Intelligence Magazine, 14(1), 2019, 16–30.
- [22] J. Kacprzyk, and S. Zadrożny. “Towards a general and unified characterization of individual and collective choice functions under fuzzy and non‐fuzzy preferences and majority via the ordered weighted average operators,” International Journal of Intelligent Systems, 24(1), 2009, 4–26.
- [23] G. Beliakov, H. B. Sola, and T. C. Sánchez. A practical guide to averaging functions, 2016, Springer.
- [24] L. Canós, and V. Liern. “Soft computing‐based aggregation methods for human resource management,” European Journal of Operational Research, 189(3), 2008, 669–681.
- [25] C.‐H. Cheng, J.‐W. Wang, and M.‐C. Wu. “OWA‐weighted based clustering method for classification problem,” Expert Systems with Applications, 36(3), 2009, 4988–4995.
- [26] M. Bernal, P. Anselmo Alvarez, M. Muñoz, E. Leon‐Castro, and D. A. Gastelum‐Chavira. “A multicriteria hierarchical approach for portfolio selection in a stock exchange,” Journal of Intelligent & Fuzzy Systems, 40(2), 2021, 1945–1955.
- [27] M. Muñoz‐Palma, P. A. Alvarez‐Carrillo, E. L. Miranda‐Espinoza, E. Avilés‐Ochoa, and E. León‐Castro, E. (2021). “Multicriteria Analysis Model for the Evaluation of the Competitiveness of the States in Mexico.” In Intelligent and Complex Systems in Economics and Business (pp. 1–20), 2021, Springer.
- [28] M. Olazabal‐Lugo, E. Leon‐Castro, L. F. Espinoza‐Audelo, J. Maria Merigo, and A. M. Gil Lafuente. “Forgotten effects and heavy moving averages in exchange rate forecasting,” Economic Computation & Economic Cybernetics Studies & Research, 53(4), 2019.
- [29] E. León‐Castro, E. Avilés‐Ochoa, and J. M. Merigó. “Induced heavy moving averages,” International Journal of Intelligent Systems, 33(9), 2018, 1823– 1839.
- [30] V. Scherger, A. Terceño, and H. Vigier. “The OWA distance operator and its application in business failure,” Kybernetes, 2017.
- [31] L. Jin and R. Mesiar. “The metric space of ordered weighted average operators with distance based on accumulated entries,” International Journal of Intelligent Systems, 32(7), 2017, 665–675.
- [32] R. R. Yager. “Heavy OWA operators,” Fuzzy optimization and decision making, 1(4), 2002, 379–397.
- [33] L. Espinoza‐Audelo, E. Aviles‐Ochoa, E. Leon‐Castro, and F. Blanco‐Mesa. “Forecastingperformance of exchange rate models with heavy moving average operators,” Fuzzy Economic Review, 24(2), 2019.
- [34] M. K. Evans. Practical business forecasting, John Wiley & Sons, 2002.
- [35] R. R. Yager, and J. Kacprzyk. The ordered weighted averaging operators: Theory and applications. Springer Science & Business Media, 2012.
- [36] J. M. Merigo, and R. R. Yager. “Generalizedmoving averages, distance measures and OWAoperators.” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(04), 2013, 533–559.
- [37] J. Kenney. Moving Averages. Princeton: Van Nostrand, 1962.
- [38] E. León‐Castro, E. Avilés‐Ochoa, and A. M. Gil Lafuente. “Exchange rate USD/MXN forecast through econometric models, time series and HOWMA operators,” Economic Computation and Economic Cybernetics Studies and Research, 50(40), 2016, 135–150.
- [39] S. Zeng, W. Su, and A. Le. “Fuzzy generalized ordered weighted averaging distance operator and its application to decision making,” International Journal of Fuzzy Systems, 14(3), 2012, 402–412.
- [40] L. Zhou, H. Chen, and J. Liu. “Generalized logarithmic proportional averaging operators and their applications to group decision making”, Knowledge Based Systems, 36, 2012, 268–279.
- [41] R. R. Yager. “Families of OWA operators”, Fuzzy Sets and Systems, 59(2), 1993, 125–148.
- [42] J. M. Merigó, and A. M. Gil‐Lafuente. “The induced generalized OWA operator,” Information Sciences, 179(6), 2009, 729–741.
- [43] E. Anderson, and R. L. Oliver. “Perspectives on behavior‐based versus outcome‐based sales‐force control systems.” Journal of Marketing, 51(4), 1987, 76–88.
- [44] C. F. Miao, and K. R. Evans. “The interactive effects of sales control systems on salesperson performance: A job demands–resources perspective,” Journal of the Academy of Marketing Science, 41(1), 2013, 73–90.
- [45] D. Baez‐Palencia, M. Olazabal‐Lugo, and J. Romero‐Muñoz. “Toma de decisiones empresariales a través de la media ordenada ponderada.” Inquietud Empresarial, 19(2), 2019, 11–23.
- [46] L. F. Espinoza‐Audelo, E. León‐Castro, M. Olazabal‐Lugo, J. M. Merigó, and A. M. Gil‐Lafuente. “Using Ordered Weighted Average for Weighted Averages Inflation,” International Journal of Information Technology & Decision Making (IJITDM), 19(02), 2020, 601–628.
- [47] M. G. Velazquez‐Cazares, A. M. Gil‐Lafuente, E. Leon‐Castro, and F. Blanco‐Mesa. “Innovation capabilities measurement using fuzzy methodologies: A Colombian SMEs case,” Computational and Mathematical Organization Theory, 27, 2021, 384–413.
- [48] M. Fedrizzi, and J. Kacprzyk. “An interactive multi‐user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers.” Decision Support Systems, 4(3), 1988, 313–327.
- [49] E. Szmidt, and J. Kacprzyk. “A consensus‐reaching process under intuitionistic fuzzy preference relations,” International Journal of Intelligent Systems, 18(7), 2003, 837–852.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4b88c88-7741-4605-aec3-3d96ca22efe2