Complementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making

Haichao Wang, Risto Lahdelma, Pekka Salminen

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
149 Downloads (Pure)


The complementary judgment matrix (CJM) method is an MCDA (multicriteria decision aiding) method based on pairwise comparisons. As in AHP, the decision-maker (DM) can specify his/her preferences using pairwise comparisons, both between different criteria and between different alternatives with respect to each criterion. The DM specifies his/her preferences by allocating two nonnegative comparison values so that their sum is 1. We measure and pinpoint possible inconsistency by inconsistency errors. We also compare the consistency of CJM and AHP trough simulation. Because preference judgments are always more or less imprecise or uncertain, we introduce a way to represent the uncertainty through stochastic distributions, and a computational method to treat the uncertainty. As in Stochastic Multicriteria Acceptability Analysis (SMAA), we consider different uncertainty levels: precise comparisons, imprecise comparisons with a stochastic distribution, and missing comparisons between criteria. We compute rank acceptability indices for the alternatives, describing the probability of an alternative to obtain a given rank considering the level of uncertainty and study the influence of the uncertainty on the SMAA-CJM results.
Original languageEnglish
Article number3695627
Number of pages11
JournalMathematical Problems in Engineering
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


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