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dc.contributor.author | Benítez López, Julio | es_ES |
dc.contributor.author | Carpitella, Silvia | es_ES |
dc.contributor.author | Certa, Antonella | es_ES |
dc.contributor.author | Izquierdo Sebastián, Joaquín | es_ES |
dc.date.accessioned | 2021-02-06T04:33:57Z | |
dc.date.available | 2021-02-06T04:33:57Z | |
dc.date.issued | 2020-09-01 | es_ES |
dc.identifier.issn | 0096-3003 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/160840 | |
dc.description.abstract | [EN] Decision-making in the presence of intangible elements must be based on a robust, but subtle, balance between expert know-how and judgment consistency when eliciting that know-how. This balance is frequently achieved as a trade-off reached after a feedback process softens the tension frequently found between one force steadily pulling towards (full) consistency, and another force driven by expert feeling and opinion. The linearization method, developed by the authors in the framework of the analytic hierarchy process, is a pull-towards-consistency mechanism that shows the path from an inconsistent body of judgment elicited from an expert towards consistency, by suggesting optimal changes to the expert opinions. However, experts may be reluctant to alter some of their issued opinions, and may wish to impose constraints on the adjustments suggested by the consistency-enforcement mechanism. In this paper, using the classical Riesz representation theorem, the linearization method is accommodated to consider various types of constraints imposed by experts during the abovementioned feedback process. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Applied Mathematics and Computation | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Decision-making | es_ES |
dc.subject | Expert judgment | es_ES |
dc.subject | Consistency | es_ES |
dc.subject | Consensus | es_ES |
dc.subject | AHP | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Constrained consistency enforcement in AHP | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.amc.2020.125273 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Benítez López, J.; Carpitella, S.; Certa, A.; Izquierdo Sebastián, J. (2020). Constrained consistency enforcement in AHP. Applied Mathematics and Computation. 380:1-12. https://doi.org/10.1016/j.amc.2020.125273 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.amc.2020.125273 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 12 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 380 | es_ES |
dc.relation.pasarela | S\407895 | es_ES |
dc.description.references | Safarzadeh, S., Khansefid, S., & Rasti-Barzoki, M. (2018). A group multi-criteria decision-making based on best-worst method. Computers & Industrial Engineering, 126, 111-121. doi:10.1016/j.cie.2018.09.011 | es_ES |
dc.description.references | Ishizaka, A., & Siraj, S. (2018). Are multi-criteria decision-making tools useful? An experimental comparative study of three methods. European Journal of Operational Research, 264(2), 462-471. doi:10.1016/j.ejor.2017.05.041 | es_ES |
dc.description.references | Yu, X., Zhang, S., Liao, X., & Qi, X. (2018). ELECTRE methods in prioritized MCDM environment. Information Sciences, 424, 301-316. doi:10.1016/j.ins.2017.09.061 | es_ES |
dc.description.references | Zareie, A., Sheikhahmadi, A., & Khamforoosh, K. (2018). Influence maximization in social networks based on TOPSIS. Expert Systems with Applications, 108, 96-107. doi:10.1016/j.eswa.2018.05.001 | es_ES |
dc.description.references | Carpitella, S., Ocaña-Levario, S. J., Benítez, J., Certa, A., & Izquierdo, J. (2018). A hybrid multi-criteria approach to GPR image mining applied to water supply system maintenance. Journal of Applied Geophysics, 159, 754-764. doi:10.1016/j.jappgeo.2018.10.021 | es_ES |
dc.description.references | Phudphad, K., Watanapa, B., Krathu, W., & Funilkul, S. (2017). Rankings of the security factors of human resources information system (HRIS) influencing the open climate of work: using analytic hierarchy process (AHP). Procedia Computer Science, 111, 287-293. doi:10.1016/j.procs.2017.06.065 | es_ES |
dc.description.references | Bertolin, C., & Loli, A. (2018). Sustainable interventions in historic buildings: A developing decision making tool. Journal of Cultural Heritage, 34, 291-302. doi:10.1016/j.culher.2018.08.010 | es_ES |
dc.description.references | Carli, R., Dotoli, M., & Pellegrino, R. (2018). A decision-making tool for energy efficiency optimization of street lighting. Computers & Operations Research, 96, 223-235. doi:10.1016/j.cor.2017.11.016 | es_ES |
dc.description.references | Huang, J., Boland, J., Liu, W., Xu, C., & Zang, H. (2018). A decision-making tool for determination of storage capacity in grid-connected PV systems. Renewable Energy, 128, 299-304. doi:10.1016/j.renene.2018.05.083 | es_ES |
dc.description.references | Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision Making in Construction Management: AHP and Expert Choice Approach. Procedia Engineering, 172, 270-276. doi:10.1016/j.proeng.2017.02.111 | es_ES |
dc.description.references | Sheng, L., Zhu, Y., & Wang, K. (2018). Uncertain dynamical system-based decision making with application to production-inventory problems. Applied Mathematical Modelling, 56, 275-288. doi:10.1016/j.apm.2017.12.006 | es_ES |
dc.description.references | Yager, R. R. (2017). Bidirectional possibilistic dominance in uncertain decision making. Knowledge-Based Systems, 133, 269-277. doi:10.1016/j.knosys.2017.06.029 | es_ES |
dc.description.references | Kozierkiewicz-Hetmańska, A. (2017). The analysis of expert opinions’ consensus quality. Information Fusion, 34, 80-86. doi:10.1016/j.inffus.2016.06.005 | es_ES |
dc.description.references | Dror, I. E., Kukucka, J., Kassin, S. M., & Zapf, P. A. (2018). When Expert Decision Making Goes Wrong: Consensus, Bias, the Role of Experts, and Accuracy. Journal of Applied Research in Memory and Cognition, 7(1), 162-163. doi:10.1016/j.jarmac.2018.01.007 | es_ES |
dc.description.references | V, S. R., & Muccini, H. (2018). Group decision-making in software architecture: A study on industrial practices. Information and Software Technology, 101, 51-63. doi:10.1016/j.infsof.2018.04.009 | es_ES |
dc.description.references | Tian, Z., Nie, R., Wang, J., & Zhang, H. (2018). A two-fold feedback mechanism to support consensus-reaching in social network group decision-making. Knowledge-Based Systems, 162, 74-91. doi:10.1016/j.knosys.2018.09.030 | es_ES |
dc.description.references | Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., & Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459, 20-35. doi:10.1016/j.ins.2018.05.017 | es_ES |
dc.description.references | Unutmaz Durmuşoğlu, Z. D. (2018). Assessment of techno-entrepreneurship projects by using Analytical Hierarchy Process (AHP). Technology in Society, 54, 41-46. doi:10.1016/j.techsoc.2018.02.001 | es_ES |
dc.description.references | Ozdemir, S., & Sahin, G. (2018). Multi-criteria decision-making in the location selection for a solar PV power plant using AHP. Measurement, 129, 218-226. doi:10.1016/j.measurement.2018.07.020 | es_ES |
dc.description.references | Russo, R. de F. S. M., & Camanho, R. (2015). Criteria in AHP: A Systematic Review of Literature. Procedia Computer Science, 55, 1123-1132. doi:10.1016/j.procs.2015.07.081 | es_ES |
dc.description.references | Franek, J., & Kresta, A. (2014). Judgment Scales and Consistency Measure in AHP. Procedia Economics and Finance, 12, 164-173. doi:10.1016/s2212-5671(14)00332-3 | es_ES |
dc.description.references | Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85-91. doi:10.1016/s0377-2217(02)00227-8 | es_ES |
dc.description.references | Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5 | es_ES |
dc.description.references | Bozóki, S., & Fülöp, J. (2018). Efficient weight vectors from pairwise comparison matrices. European Journal of Operational Research, 264(2), 419-427. doi:10.1016/j.ejor.2017.06.033 | es_ES |
dc.description.references | Szybowski, J. (2018). The improvement of data in pairwise comparison matrices. Procedia Computer Science, 126, 1006-1013. doi:10.1016/j.procs.2018.08.036 | es_ES |
dc.description.references | Benítez, J., Carpitella, S., Certa, A., Ilaya-Ayza, A. E., & Izquierdo, J. (2018). Consistent clustering of entries in large pairwise comparison matrices. Journal of Computational and Applied Mathematics, 343, 98-112. doi:10.1016/j.cam.2018.04.041 | es_ES |
dc.description.references | Benítez, J., Delgado-Galván, X., Izquierdo, J., & Pérez-García, R. (2011). Achieving matrix consistency in AHP through linearization. Applied Mathematical Modelling, 35(9), 4449-4457. doi:10.1016/j.apm.2011.03.013 | es_ES |
dc.description.references | Benítez, J., Delgado-Galván, X., Gutiérrez, J. A., & Izquierdo, J. (2011). Balancing consistency and expert judgment in AHP. Mathematical and Computer Modelling, 54(7-8), 1785-1790. doi:10.1016/j.mcm.2010.12.023 | es_ES |
dc.description.references | Benítez, J., Izquierdo, J., Pérez-García, R., & Ramos-Martínez, E. (2014). A simple formula to find the closest consistent matrix to a reciprocal matrix. Applied Mathematical Modelling, 38(15-16), 3968-3974. doi:10.1016/j.apm.2014.01.007 | es_ES |