The analytic hierarchy process with stochastic judgements

Ian Noel Durbach*, Risto Lahdelma, Pekka Salminen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)

Abstract

The analytic hierarchy process (AHP) is a widely-used method for multicriteria decision support based on the hierarchical decomposition of objectives, evaluation of preferences through pairwise comparisons, and a subsequent aggregation into global evaluations. The current paper integrates the AHP with stochastic multicriteria acceptability analysis (SMAA), an inverse-preference method, to allow the pairwise comparisons to be uncertain. A simulation experiment is used to assess how the consistency of judgements and the ability of the SMAA-AHP model to discern the best alternative deteriorates as uncertainty increases. Across a range of simulated problems results indicate that, according to conventional benchmarks, judgements are likely to remain consistent unless uncertainty is severe, but that the presence of uncertainty in almost any degree is sufficient to make the choice of best alternative unclear.
Original languageEnglish
Pages (from-to)552-559
Number of pages8
JournalEuropean Journal of Operational Research
Volume238
Issue number2
Early online date13 Apr 2014
DOIs
Publication statusPublished - Oct 2014

Keywords

  • Decision analysis
  • Multicriteria
  • Analytic hierarchy process
  • Uncertainty
  • Simulation

Fingerprint

Dive into the research topics of 'The analytic hierarchy process with stochastic judgements'. Together they form a unique fingerprint.

Cite this