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Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE

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  • Corrente, Salvatore
  • Greco, Salvatore
  • Słowiński, Roman
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    Abstract

    Robust Ordinal Regression (ROR) supports Multiple Criteria Decision Process by considering all sets of parameters of an assumed preference model, that are compatible with preference information elicited by a Decision Maker (DM). As a result of ROR, one gets necessary and possible preference relations in the set of alternatives, which hold for all compatible sets of parameters, or for at least one compatible set of parameters, respectively. In this paper, we propose an extension of ELECTRE and PROMETHEE methods to the case of the hierarchy of criteria, which was never considered before. Then, we adapt ROR to the hierarchical versions of ELECTRE and PROMETHEE methods.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 41 (2013)
    Issue (Month): 5 ()
    Pages: 820-846

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    Handle: RePEc:eee:jomega:v:41:y:2013:i:5:p:820-846

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    Keywords: Hierarchy of criteria; ELECTRE methods; PROMETHEE methods; Robust Ordinal Regression;

    References

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    1. Kadziński, MiŁosz & Greco, Salvatore & SŁowiński, Roman, 2012. "Extreme ranking analysis in robust ordinal regression," Omega, Elsevier, vol. 40(4), pages 488-501.
    2. Greco, Salvatore & Mousseau, Vincent & Slowinski, Roman, 2010. "Multiple criteria sorting with a set of additive value functions," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1455-1470, December.
    3. Figueira, José Rui & Greco, Salvatore & Slowinski, Roman, 2009. "Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method," European Journal of Operational Research, Elsevier, vol. 195(2), pages 460-486, June.
    4. Ergu, Daji & Kou, Gang & Peng, Yi & Shi, Yong, 2011. "A simple method to improve the consistency ratio of the pair-wise comparison matrix in ANP," European Journal of Operational Research, Elsevier, vol. 213(1), pages 246-259, August.
    5. Dov Pekelman & Subrata K. Sen, 1974. "Mathematical Programming Models for the Determination of Attribute Weights," Management Science, INFORMS, vol. 20(8), pages 1217-1229, April.
    6. Angilella, Silvia & Greco, Salvatore & Matarazzo, Benedetto, 2010. "Non-additive robust ordinal regression: A multiple criteria decision model based on the Choquet integral," European Journal of Operational Research, Elsevier, vol. 201(1), pages 277-288, February.
    7. V. Srinivasan & Allan Shocker, 1973. "Estimating the weights for multiple attributes in a composite criterion using pairwise judgments," Psychometrika, Springer, vol. 38(4), pages 473-493, December.
    8. Jacquet-Lagreze, E. & Siskos, J., 1982. "Assessing a set of additive utility functions for multicriteria decision-making, the UTA method," European Journal of Operational Research, Elsevier, vol. 10(2), pages 151-164, June.
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    Cited by:
    1. Kou, Gang & Lin, Changsheng, 2014. "A cosine maximization method for the priority vector derivation in AHP," European Journal of Operational Research, Elsevier, vol. 235(1), pages 225-232.
    2. Feng, Bo & Lai, Fujun, 2014. "Multi-attribute group decision making with aspirations: A case study," Omega, Elsevier, vol. 44(C), pages 136-147.
    3. Ishizaka, Alessio & Nemery, Philippe, 2014. "Assigning machines to incomparable maintenance strategies with ELECTRE-SORT," Omega, Elsevier, vol. 47(C), pages 45-59.
    4. Spliet, Remy & Tervonen, Tommi, 2014. "Preference inference with general additive value models and holistic pair-wise statements," European Journal of Operational Research, Elsevier, vol. 232(3), pages 607-612.
    5. Roy, B. & Figueira, J.R. & Almeida-Dias, J., 2014. "Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: Theoretical results and practical issues," Omega, Elsevier, vol. 43(C), pages 9-20.

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