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Coherent weights for pairwise comparison matrices and a mixed-integer linear programming problem

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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.

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References

  1. 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)

    Article  MathSciNet  MATH  Google Scholar 

  2. Barzilai, J.: Deriving weights from pairwise comparison matrices. J. Oper. Res. Soc. 48(12), 1226–1232 (1997)

    Article  MATH  Google Scholar 

  3. 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)

    Article  MathSciNet  MATH  Google Scholar 

  4. Barzilai, J., Golany, B.: Deriving weights from pairwise comparison matrices: the additive case. Oper. Res. Lett. 9(6), 407–410 (1990)

    Article  MATH  Google Scholar 

  5. Birkhoff, G.: Lattice Theory, vol. 25. American Mathematical Society, Providence (1948)

    MATH  Google Scholar 

  6. Bozóki, S., Rapcsák, T.: On Saaty’s and Koczkodaj’s inconsistencies of pairwise comparison matrices. J. Glob. Optim. 42(2), 157–175 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  7. Carrizosa, E., Messine, F.: An exact global optimization method for deriving weights from pairwise comparison matrices. J. Glob. Optim. 38(2), 237–247 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. 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)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cavallo, B.: Computing random consistency indices and assessing priority vectors reliability. Inf. Sci. 420, 532–542 (2017)

    Article  Google Scholar 

  10. 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)

    Article  MathSciNet  MATH  Google Scholar 

  11. 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)

    Article  MATH  Google Scholar 

  12. Cavallo, B., D’Apuzzo, L.: Deriving weights from a pairwise comparison matrix over an Alo-group. Soft Comput. 16(2), 353–366 (2012)

    Article  MATH  Google Scholar 

  13. 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)

    Article  MathSciNet  MATH  Google Scholar 

  14. Cavallo, B., D’Apuzzo, L.: Ensuring reliability of the weighting vector: weak consistent pairwise comparison matrices. Fuzzy Sets Syst. 296, 21–34 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  15. Cavallo, B., D’Apuzzo, L., Basile, L.: Weak consistency for ensuring priority vectors reliability. J. Multi-Criteria Decis. Anal. 283, 126–138 (2016)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Cook, W.D., Kress, M.: Deriving weights from pairwise comparison ratio matrices: an axiomatic approach. Eur. J. Oper. Res. 37(3), 355–362 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  19. Crawford, G., Williams, C.: A note on the analysis of subjective judgment matrices. J. Math. Psychol. 29(4), 387–405 (1985)

    Article  MATH  Google Scholar 

  20. Csató, L.: Characterization of the row geometric mean ranking with a group consensus axiom. Group Decis. Negot. 27(6), 1011–1027 (2018)

    Article  MathSciNet  Google Scholar 

  21. Csató, L.: A characterization of the logarithmic least squares method. Eur. J. Oper. Res. 276(1), 212–216 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  22. De Graan, J.G.: Extensions of the multiple criteria analysis method of T. L. Saaty. Report, National Institute for Water Supply, Voorburg (1980)

  23. Dijkstra, T.K.: On the extraction of weights from pairwise comparison matrices. Cent. Eur. J. Oper. Res. 21(1), 103–123 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  24. 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)

  25. Fichtner, J.: On deriving priority vectors from matrices of pairwise comparisons. Socio-Econ. Plan. Sci. 20(6), 341–345 (1986)

    Article  Google Scholar 

  26. Fraleigh, J.B.: A First Course in Abstract Algebra, 7th edn. Pearson, London (2002)

    MATH  Google Scholar 

  27. Fülöp, J.: A method for approximating pairwise comparison matrices by consistent matrices. J. Glob. Optim. 42(3), 423–442 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  28. 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)

    Article  MathSciNet  MATH  Google Scholar 

  29. Hou, F.: A multiplicative Alo-group based hierarchical decision model and application. Commun. Stat. Simul. Comput. 45(8), 2846–2862 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  30. Ishizaka, A., Lusti, M.: How to derive priorities in AHP: a comparative study. Cent. Eur. J. Oper. Res. 14(4), 387–400 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  31. 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)

    Article  MATH  Google Scholar 

  32. 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)

    Article  MathSciNet  MATH  Google Scholar 

  33. 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)

    Article  MathSciNet  MATH  Google Scholar 

  34. Rabinowitz, G.: Some comments on measuring world influence. J. Peace Sci. 2(1), 49–55 (1976)

    Article  MathSciNet  Google Scholar 

  35. Ramík, J.: Isomorphisms between fuzzy pairwise comparison matrices. Fuzzy Optim. Decis. Mak. 14(2), 199–209 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  36. Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  37. Tanino, T.: Fuzzy preference orderings in group decision making. Fuzzy Sets Syst. 12(2), 117–131 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  38. 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)

    Article  MathSciNet  MATH  Google Scholar 

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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:

$$\begin{aligned} m_{{\mathcal {G}}}\left( \mathbf{g }_{i}\right) = \left( \bigodot _{k=1}^n g_{ik}\right) ^{\left( \frac{1}{n}\right) } \ge \left( \bigodot _{k=1}^n g_{jk}\right) ^{\left( \frac{1}{n}\right) } = m_{{\mathcal {G}}}\left( \mathbf{g }_{j}\right) . \end{aligned}$$

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, \(m_{{\mathcal {G}}}( \mathbf{g }_{i})> m_{{\mathcal {G}}}( \mathbf{g }_{j})\). \(\square \)

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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

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