Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis

Tommi Tervonen, Gert van Vakenhoef*, Nalan Basturk, Douwe Postmus

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

92 Citations (Web of Science)


Models for multiple criteria decision analysis (mcda) often separate per-criterion attractiveness evaluation from weighted aggregation of these evaluations across the different criteria. In simulation-based mcda methods, such as stochastic multicriteria acceptability analysis, uncertainty in the weights is modeled through a uniform distribution on the feasible weight space defined by a set of linear constraints. Efficient sampling methods have been proposed for special cases, such as the unconstrained weight space or complete ordering of the weights. However, no efficient methods are available for other constraints such as imprecise trade-off ratios, and specialized sampling methods do not allow for flexibility in combining the different constraint types. In this paper, we explore how the hit-and-run sampler can be applied as a general approach for sampling from the convex weight space that results from an arbitrary combination of linear weight constraints. We present a technique for transforming the weight space to enable application of hit-and-run, and evaluate the sampler’s efficiency through computational tests. Our results show that the thinning factor required to obtain uniform samples can be expressed as a function of the number of criteria n as f(n) = (n - 1)3. We also find that the technique is reasonably fast with problem sizes encountered in practice and that autocorrelation is an appropriate convergence metric.
Original languageEnglish
Pages (from-to)552-559
JournalEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 1 Feb 2013
Externally publishedYes


  • Multiple criteria analysis
  • Simulation
  • Uncertainty modeling
  • Markov Chain Monte Carlo
  • Stochastic Multicriteria Acceptability Analysis (SMAA)

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