- -

Constrained consistency enforcement in AHP

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Constrained consistency enforcement in AHP

Mostrar el registro sencillo del ítem

Ficheros en el ítem

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


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem