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

Author

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  • Tervonen, Tommi
  • van Valkenhoef, Gert
  • Baştürk, Nalan
  • Postmus, Douwe

Abstract

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 φ(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.

Suggested Citation

  • Tervonen, Tommi & van Valkenhoef, Gert & Baştürk, Nalan & Postmus, Douwe, 2013. "Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 224(3), pages 552-559.
  • Handle: RePEc:eee:ejores:v:224:y:2013:i:3:p:552-559
    DOI: 10.1016/j.ejor.2012.08.026
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    References listed on IDEAS

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    2. Vetschera, Rudolf, 2017. "Deriving rankings from incomplete preference information: A comparison of different approaches," European Journal of Operational Research, Elsevier, vol. 258(1), pages 244-253.
    3. Corrente, Salvatore & Figueira, José Rui & Greco, Salvatore, 2014. "The SMAA-PROMETHEE method," European Journal of Operational Research, Elsevier, vol. 239(2), pages 514-522.
    4. Durbach, Ian N. & Calder, Jon M., 2016. "Modelling uncertainty in stochastic multicriteria acceptability analysis," Omega, Elsevier, vol. 64(C), pages 13-23.
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    6. Mavrotas, George & Pechak, Olena & Siskos, Eleftherios & Doukas, Haris & Psarras, John, 2015. "Robustness analysis in Multi-Objective Mathematical Programming using Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 240(1), pages 193-201.
    7. Angilella, Silvia & Corrente, Salvatore & Greco, Salvatore, 2015. "Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem," European Journal of Operational Research, Elsevier, vol. 240(1), pages 172-182.
    8. Durbach, Ian & Lahdelma, Risto & Salminen, Pekka, 2014. "The analytic hierarchy process with stochastic judgements," European Journal of Operational Research, Elsevier, vol. 238(2), pages 552-559.
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    11. Kadziński, Miłosz & Tervonen, Tommi & Rui Figueira, José, 2015. "Robust multi-criteria sorting with the outranking preference model and characteristic profiles," Omega, Elsevier, vol. 55(C), pages 126-140.
    12. van Valkenhoef, Gert & Tervonen, Tommi, 2016. "Entropy-optimal weight constraint elicitation with additive multi-attribute utility models," Omega, Elsevier, vol. 64(C), pages 1-12.
    13. Kadziński, Miłosz & Tervonen, Tommi, 2013. "Robust multi-criteria ranking with additive value models and holistic pair-wise preference statements," European Journal of Operational Research, Elsevier, vol. 228(1), pages 169-180.
    14. Kadziński, Miłosz & Labijak, Anna & Napieraj, Małgorzata, 2017. "Integrated framework for robustness analysis using ratio-based efficiency model with application to evaluation of Polish airports," Omega, Elsevier, vol. 67(C), pages 1-18.
    15. Doumpos, Michael & Zopounidis, Constantin & Galariotis, Emilios, 2014. "Inferring robust decision models in multicriteria classification problems: An experimental analysis," European Journal of Operational Research, Elsevier, vol. 236(2), pages 601-611.
    16. Mastorakis, Kostis & Siskos, Eleftherios, 2016. "Value focused pharmaceutical strategy determination with multicriteria decision analysis techniques," Omega, Elsevier, vol. 59(PA), pages 84-96.
    17. repec:eee:jomega:v:71:y:2017:i:c:p:27-45 is not listed on IDEAS
    18. Aur'elien Hazan, 2017. "Stock-flow consistent macroeconomic model with nonuniform distributional constraint," Papers 1708.00645, arXiv.org.
    19. Angilella, Silvia & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman, 2016. "Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model," Omega, Elsevier, vol. 63(C), pages 154-169.
    20. Hazan, Aurélien, 2017. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 589-602.
    21. Zheng, Buhong & Zheng, Charles, 2015. "Fuzzy ranking of human development: A proposal," Mathematical Social Sciences, Elsevier, vol. 78(C), pages 39-47.
    22. repec:spr:grdene:v:27:y:2018:i:1:d:10.1007_s10726-017-9549-3 is not listed on IDEAS
    23. Gryazina, Elena & Polyak, Boris, 2014. "Random sampling: Billiard Walk algorithm," European Journal of Operational Research, Elsevier, vol. 238(2), pages 497-504.
    24. Aur'elien Hazan, 2016. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Papers 1601.00822, arXiv.org, revised Jan 2017.
    25. van Valkenhoef, Gert & Tervonen, Tommi & Postmus, Douwe, 2014. "Notes on ‘Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis’," European Journal of Operational Research, Elsevier, vol. 239(3), pages 865-867.

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