Abstract
Pairwise comparison matrices (PCMs) have been a long standing technique for comparing alternatives/criteria and their role has been pivotal in the development of modern decision making methods. In order to obtain general results, suitable for several kinds of PCMs proposed in the literature, we focus on PCMs defined over a general unifying framework, that is an Abelian linearly ordered group. The paper deals with a crucial step in multi-criteria decision analysis, that is to obtain coherent weights for alternatives/criteria that are compared by means of a PCM. Firstly, we provide a condition ensuring coherent weights. Then, we provide and solve a mixed-integer linear programming problem in order to obtain the closest PCM, to a given PCM, having coherent weights. Isomorphisms and the mixed-integer linear programming problem allow us to solve an infinity of optimization problems, among them optimization problems concerning additive, multiplicative and fuzzy PCMs.
Similar content being viewed by others
References
Bana e Costa, C.A., Vansnick, J.C.: A critical analysis of the eigenvalue method used to derive priorities in AHP. Eur. J. Oper. Res. 187(3), 1422–1428 (2008)
Barzilai, J.: Deriving weights from pairwise comparison matrices. J. Oper. Res. Soc. 48(12), 1226–1232 (1997)
Barzilai, J., Cook, W., Golany, B.: Consistent weights for judgements matrices of the relative importance of alternatives. Oper. Res. Lett. 6(3), 131–134 (1987)
Barzilai, J., Golany, B.: Deriving weights from pairwise comparison matrices: the additive case. Oper. Res. Lett. 9(6), 407–410 (1990)
Birkhoff, G.: Lattice Theory, vol. 25. American Mathematical Society, Providence (1948)
Bozóki, S., Rapcsák, T.: On Saaty’s and Koczkodaj’s inconsistencies of pairwise comparison matrices. J. Glob. Optim. 42(2), 157–175 (2008)
Carrizosa, E., Messine, F.: An exact global optimization method for deriving weights from pairwise comparison matrices. J. Glob. Optim. 38(2), 237–247 (2007)
Cavallo, B.: A further discussion of “A semiring-based study of judgment matrices: properties and models” [Information Sciences 181 (2011) 2166–2176]. Inf. Sci. 287, 61–67 (2014)
Cavallo, B.: Computing random consistency indices and assessing priority vectors reliability. Inf. Sci. 420, 532–542 (2017)
Cavallo, B.: \({\cal{G}}\)-distance and \({\cal{G}}\)-decomposition for improving \({\cal{G}}\)-consistency of a pairwise comparison matrix. Fuzzy Optim. Decis. Mak. 18(1), 57–83 (2019)
Cavallo, B., D’Apuzzo, L.: A general unified framework for pairwise comparison matrices in multicriterial methods. Int. J. Intell. Syst. 24(4), 377–398 (2009)
Cavallo, B., D’Apuzzo, L.: Deriving weights from a pairwise comparison matrix over an Alo-group. Soft Comput. 16(2), 353–366 (2012)
Cavallo, B., D’Apuzzo, L.: Reciprocal transitive matrices over abelian linearly ordered groups: characterizations and application to multi-criteria decision problems. Fuzzy Sets Syst. 266, 33–46 (2015)
Cavallo, B., D’Apuzzo, L.: Ensuring reliability of the weighting vector: weak consistent pairwise comparison matrices. Fuzzy Sets Syst. 296, 21–34 (2016)
Cavallo, B., D’Apuzzo, L., Basile, L.: Weak consistency for ensuring priority vectors reliability. J. Multi-Criteria Decis. Anal. 283, 126–138 (2016)
Cavallo, B., Ishizaka, A., Olivieri, M.G., Squillante, M.: Comparing inconsistency of pairwise comparison matrices depending on entries. J. Oper. Res. Soc. 70(5), 842–850 (2019)
Chiclana, F., Herrera-Viedma, E., Alonso, S., Herrera, F.: Cardinal consistency of reciprocal preference relations: a characterization of multiplicative transitivity. IEEE Trans. Fuzzy Syst. 17(1), 14–23 (2009)
Cook, W.D., Kress, M.: Deriving weights from pairwise comparison ratio matrices: an axiomatic approach. Eur. J. Oper. Res. 37(3), 355–362 (1988)
Crawford, G., Williams, C.: A note on the analysis of subjective judgment matrices. J. Math. Psychol. 29(4), 387–405 (1985)
Csató, L.: Characterization of the row geometric mean ranking with a group consensus axiom. Group Decis. Negot. 27(6), 1011–1027 (2018)
Csató, L.: A characterization of the logarithmic least squares method. Eur. J. Oper. Res. 276(1), 212–216 (2019)
De Graan, J.G.: Extensions of the multiple criteria analysis method of T. L. Saaty. Report, National Institute for Water Supply, Voorburg (1980)
Dijkstra, T.K.: On the extraction of weights from pairwise comparison matrices. Cent. Eur. J. Oper. Res. 21(1), 103–123 (2013)
Fichtner, J.: Some thoughts about the mathematics of the Analytic Hierarchy Process. Technical report, Institut für Angewandte Systemforschung und Operations Research, Universität der Bundeswehr München (1984)
Fichtner, J.: On deriving priority vectors from matrices of pairwise comparisons. Socio-Econ. Plan. Sci. 20(6), 341–345 (1986)
Fraleigh, J.B.: A First Course in Abstract Algebra, 7th edn. Pearson, London (2002)
Fülöp, J.: A method for approximating pairwise comparison matrices by consistent matrices. J. Glob. Optim. 42(3), 423–442 (2008)
Herrera-Viedma, E., Herrera, F., Chiclana, F., Luque, M.: Some issues on consistency of fuzzy preference relations. Eur. J. Oper. Res. 154(1), 98–109 (2004)
Hou, F.: A multiplicative Alo-group based hierarchical decision model and application. Commun. Stat. Simul. Comput. 45(8), 2846–2862 (2016)
Ishizaka, A., Lusti, M.: How to derive priorities in AHP: a comparative study. Cent. Eur. J. Oper. Res. 14(4), 387–400 (2006)
Jones, D.F., Mardle, S.J.: A distance-metric methodology for the derivation of weights from a pairwise comparison matrix. J. Oper. Res. Soc. 55(8), 869–875 (2004)
Kułakowski, K., Mazurek, J., Ramík, J., Soltys, M.: When is the condition of order preservation met? Eur. J. Oper. Res. 277(1), 248–254 (2019)
Lundy, M., Siraj, S., Greco, S.: The mathematical equivalence of the ”spanning tree” and row geometric mean preference vectors and its implications for preference analysis. Eur. J. Oper. Res. 257(1), 197–208 (2017)
Rabinowitz, G.: Some comments on measuring world influence. J. Peace Sci. 2(1), 49–55 (1976)
Ramík, J.: Isomorphisms between fuzzy pairwise comparison matrices. Fuzzy Optim. Decis. Mak. 14(2), 199–209 (2015)
Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977)
Tanino, T.: Fuzzy preference orderings in group decision making. Fuzzy Sets Syst. 12(2), 117–131 (1984)
Xia, M., Chen, J.: Consistency and consensus improving methods for pairwise comparison matrices based on Abelian linearly ordered group. Fuzzy Sets Syst. 266, 1–32 (2015)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Proof of Theorem 1
\(1 \Leftrightarrow 2\). The direct implication is straightforward; we have to prove \(2\Rightarrow 1\), that is the second equivalence in (17).
Let us assume \(w_{i}=w_{j}\). If ad absurdum \(g_{ij} \ne e\) then \(w_{i}>w_{j}\) (if \(g_{ij} > e\)) or \(w_{i}<w_{j}\) (if \(g_{ij} < e\)); it is against the assumption. Thus, \(g_{ij} = e\). Vice-versa, let us assume \(g_{ij} = e\). If ad absurdum \(w_{i}\ne w_{j}\) then \(g_{ij} > e\) (if \(w_{i}>w_{j}\)) or \(g_{ij} < e\) (if \(w_{i}<w_{j}\)); it is against the assumption. Thus, \(w_{i}=w_{j}\) and the assertion is achieved.
\(1 \Leftrightarrow 3\). The direct implication is straightforward; we have to prove \(3\Rightarrow 1\).
Let us assume \(w_{i}>w_{j}\). If ad absurdum \(g_{ij} \le e\) then \(w_{i}=w_{j}\) (if \(g_{ij} = e\)) or \(w_{i}<w_{j}\) (if \(g_{ij} < e\)); it is against the assumption. Thus, \(g_{ij} >e\). Let us assume \(w_{i}=w_{j}\). If ad absurdum \(g_{ij} \ne e\) then \(w_{i}>w_{j}\) (if \(g_{ij} > e\)) or \(w_{i}<w_{j}\) (if \(g_{ij}< e\)); it is against the assumption. Thus, \(g_{ij} = e\) and the assertion is achieved. \(\square \)
Proof of Theorem 2
\(1 \Rightarrow 2\). Let us assume that \(\mathbf{G }=(g_{ij})_{n \times n}\) is a restricted max–max \({\mathcal {G}}\)-transitive PCM (i.e. \(\mathbf{G }=(g_{ij})_{n \times n}\) satisfies (20)).
Let \(g_{ij}\ge e\). By (20), \(g_{ik}\ge \max \{ g_{ij},g_{jk} \} \ge g_{jk}\) for all \(g_{jk} \ge e \). Let us assume that \(g_{jk} <e \). If ad absurdum, \(g_{ik} < g_{jk}\), then, by \(g_{ki}>e\), \(g_{ij}\ge e\) and (20), \(g_{kj}\ge \max \{ g_{ki},g_{ij} \} \ge g_{ki}\). Thus, by \({\mathcal {G}}\)-reciprocity, \( g_{ik} \ge g_{jk}\); it contradicts \(g_{ik} < g_{jk}\). Thus, \(g_{ik} \ge g_{jk}, \) for all \(k \in \{1, \ldots , n\}\).
Vice versa, if \(g_{ik} \ge g_{jk}\) for all \(k \in \{1, \ldots , n\}\), then for \(k=j\), \(g_{ij} \ge g_{jj}=e\).
\(2 \Rightarrow 1\). Let \(g_{ij} \ge e \text { and } g_{jk} \ge e\).
Let us assume ad absurdum that \(g_{ik}< \max \{ g_{ij},g_{jk} \}\). If \(g_{ik}<\max \{ g_{ij},g_{jk} \}= g_{ij}\), then by \(g_{jk} \ge e\) and 2., we have \(g_{jl} \ge g_{kl} \) for all \( l \in \{1, \ldots , n\}\), and, for \(l=i\), \(g_{ji} \ge g_{ki}\). Thus, by \({\mathcal {G}}\)-reciprocity, \(g_{ik} \ge g_{ij}\); it contradicts \(g_{ik}< g_{ij}\). If \(g_{ik}<\max \{ g_{ij},g_{jk} \}= g_{jk}\), then by \(g_{ij} \ge e\) and 2., we have \(g_{il} \ge g_{jl} \) for all \( l \in \{1, \ldots , n\}\), and, for \(l=k\), \(g_{ik} \ge g_{jk}\); it contradicts \(g_{ik}< g_{jk}\).
Thus, 1. is achieved. \(\square \)
Proof of Corollary 1
Let \(g_{ij} =e\). By Theorem 2, \(g_{ik} \ge g_{jk}\) and \(g_{jk} \ge g_{ik}, \; \forall k \in \{1, \ldots , n\}\); thus, \(g_{ik} =g_{jk} \) for all \( k \in \{1, \ldots , n\}\).
Let \(g_{ik} =g_{jk} \) for all \( k \in \{1, \ldots , n\}\). For \(k=j\), \(g_{ij} =g_{jj}=e\). \(\square \)
Proof of Proposition 2
By Theorem 1, it is enough to prove \( m_{{\mathcal {G}}}( \mathbf{g }_{i})> m_{{\mathcal {G}}}( \mathbf{g }_{j}) \Leftrightarrow g_{ij}>e.\)
Let \(m_{{\mathcal {G}}}( \mathbf{g }_{i})> m_{{\mathcal {G}}}( \mathbf{g }_{j})\). If ad absurdum \(g_{ij}\le e\), then by \({\mathcal {G}}\)-reciprocity \(g_{ji}\ge e\) and, by Theorem 2, \(g_{jk} \ge g_{ik}, \) for all \( k \in \{1, \ldots , n\}\) and, as a consequence, \(m_{{\mathcal {G}}}( \mathbf{g }_{j})\ge m_{{\mathcal {G}}}( \mathbf{g }_{i})\); it contradicts \(m_{{\mathcal {G}}}( \mathbf{g }_{i})> m_{{\mathcal {G}}}( \mathbf{g }_{j})\).
Let \(g_{ij}>e\). By Theorem 2, \(g_{ik} \ge g_{jk}, \) for all \( k \in \{1, \ldots , n\}\); thus:
In order to prove that \(m_{{\mathcal {G}}}( \mathbf{g }_{i}) > m_{{\mathcal {G}}}( \mathbf{g }_{j})\), it is enough to prove that there exists a value \({\overline{k}}\) such that \(g_{i{\overline{k}}} >g_{j{\overline{k}}} \). If ad absurdum, \(g_{ik}=g_{jk}\;\) for all \( k \in \{1, \ldots , n\}\), then, for \(k=j\), \(g_{ij}=g_{jj}=e\); it contradicts the assumption \(g_{ij}>e\). Thus, \(m_{{\mathcal {G}}}( \mathbf{g }_{i})> m_{{\mathcal {G}}}( \mathbf{g }_{j})\). \(\square \)
Rights and permissions
About this article
Cite this article
Cavallo, B. Coherent weights for pairwise comparison matrices and a mixed-integer linear programming problem. J Glob Optim 75, 143–161 (2019). https://doi.org/10.1007/s10898-019-00797-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10898-019-00797-8