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Application of stochastic analytic hierarchy process within a domestic appliance manufacturer

Author

Listed:
  • R Bañuelas

    (University of Warwick)

  • J Antony

    (Glasgow Caledonian University)

Abstract

The stochastic analytic hierarchy process (SAHP) provides a mechanism for achieving more effective selection of alternatives in the form of considering multi and conflicting criteria using quantitative and qualitative information under uncertainty. In contrast to the traditional analytic hierarchy process, the SAHP uses probabilistic distributions to incorporate uncertainty that people have in converging their judgements of preferences into a Likert scale. The vector of priorities is calculated using Monte Carlo simulation, the final rankings are analysed for rank reversal using statistical analysis, and managerial aspects are introduced systematically. The present paper demonstrates an application of the SAHP in a world-class domestic appliance manufacturer. The case study was carried out by strictly following a disciplined and organized methodology for applying the SAHP developed by the authors. The results of this study were encouraging to key personnel within the company, establishing a greater opportunity to explore the applications of the SAHP in other core business processes.

Suggested Citation

  • R Bañuelas & J Antony, 2007. "Application of stochastic analytic hierarchy process within a domestic appliance manufacturer," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 29-38, January.
  • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:1:d:10.1057_palgrave.jors.2602060
    DOI: 10.1057/palgrave.jors.2602060
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    References listed on IDEAS

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    1. Vargas, Luis G., 1990. "An overview of the analytic hierarchy process and its applications," European Journal of Operational Research, Elsevier, vol. 48(1), pages 2-8, September.
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    Cited by:

    1. Ian Durbach, 2019. "Scenario planning in the analytic hierarchy process," Futures & Foresight Science, John Wiley & Sons, vol. 1(2), June.
    2. Jessop, Alan, 2014. "IMP: A decision aid for multiattribute evaluation using imprecise weight estimates," Omega, Elsevier, vol. 49(C), pages 18-29.
    3. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    4. 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.
    5. A Ishizaka & D Balkenborg & T Kaplan, 2011. "Influence of aggregation and measurement scale on ranking a compromise alternative in AHP," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 700-710, April.
    6. Mingers, John, 2011. "Soft OR comes of age--but not everywhere!," Omega, Elsevier, vol. 39(6), pages 729-741, December.
    7. A Jessop, 2011. "Using imprecise estimates for weights," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1048-1055, June.
    8. Fan, Zhi-Ping & Liu, Yang & Feng, Bo, 2010. "A method for stochastic multiple criteria decision making based on pairwise comparisons of alternatives with random evaluations," European Journal of Operational Research, Elsevier, vol. 207(2), pages 906-915, December.

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