6533b824fe1ef96bd128008f
RESEARCH PRODUCT
Complementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making
Haichao WangRisto LahdelmaPekka Salminensubject
Mathematical optimizationArticle SubjectComputer scienceGeneral Mathematicsstokastinen monikriteerinen arvostusanalyysi0211 other engineering and technologiesAnalytic hierarchy processcomparisons02 engineering and technologyMeasure (mathematics)Consistency (database systems)0202 electrical engineering electronic engineering information engineeringuncertainty levelsPreference (economics)ta512päätösteoriaStochastic multicriteria acceptability analysis021103 operations researchta214complementary judgment matrix (CJM) methodlcsh:MathematicsRank (computer programming)ta111General EngineeringMultiple-criteria decision analysislcsh:QA1-939epävarmuuslcsh:TA1-2040stochastic multicriteria acceptability analysis (SMAA)020201 artificial intelligence & image processingPairwise comparisonlcsh:Engineering (General). Civil engineering (General)multicriteria decision-makingmatriisitdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-10-09 |