IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v245y2016i1d10.1007_s10479-014-1657-8.html
   My bibliography  Save this article

Determining attribute weights to improve solution reliability and its application to selecting leading industries

Author

Listed:
  • Chao Fu

    (Hefei University of Technology, Hefei
    Ministry of Education, Hefei)

  • Dong-Ling Xu

    (The University of Manchester)

Abstract

In multiple attribute decision analysis, many methods have been proposed to determine attribute weights. However, solution reliability is rarely considered in those methods. This paper develops an objective method in the context of the evidential reasoning approach to determine attribute weights which achieve high solution reliability. Firstly, the minimal satisfaction indicator of each alternative on each attribute is constructed using the performance data of each alternative. Secondly, the concept of superior intensity of an alternative is introduced and constructed using the minimal satisfaction of each alternative. Thirdly, the concept of solution reliability on each attribute is defined as the ordered weighted averaging (OWA) of the superior intensity of each alternative. Fourthly, to calculate the solution reliability on each attribute, the methods for determining the weights of the OWA operator are developed based on the minimax disparity method. Then, each attribute weight is calculated by letting it be proportional to the solution reliability on that attribute. A problem of selecting leading industries is investigated to demonstrate the applicability and validity of the proposed method. Finally, the proposed method is compared with other four methods using the problem, which demonstrates the high solution reliability of the proposed method.

Suggested Citation

  • Chao Fu & Dong-Ling Xu, 2016. "Determining attribute weights to improve solution reliability and its application to selecting leading industries," Annals of Operations Research, Springer, vol. 245(1), pages 401-426, October.
    • Handle: RePEc:spr:annopr:v:245:y:2016:i:1:d:10.1007_s10479-014-1657-8
    DOI: 10.1007/s10479-014-1657-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-014-1657-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-014-1657-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shirland, Larry E. & Jesse, Richard R. & Thompson, Ronald L. & Iacovou, Charalambos L., 2003. "Determining attribute weights using mathematical programming," Omega, Elsevier, vol. 31(6), pages 423-437, December.
    2. Takeda, E. & Cogger, K. O. & Yu, P. L., 1987. "Estimating criterion weights using eigenvectors: A comparative study," European Journal of Operational Research, Elsevier, vol. 29(3), pages 360-369, June.
    3. Kocher, Martin G. & Sutter, Matthias, 2006. "Time is money--Time pressure, incentives, and the quality of decision-making," Journal of Economic Behavior & Organization, Elsevier, vol. 61(3), pages 375-392, November.
    4. Xu, Xiaozhan, 2004. "A note on the subjective and objective integrated approach to determine attribute weights," European Journal of Operational Research, Elsevier, vol. 156(2), pages 530-532, July.
    5. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    6. Caroline Mota & Adiel Almeida, 2012. "A multicriteria decision model for assigning priority classes to activities in project management," Annals of Operations Research, Springer, vol. 199(1), pages 361-372, October.
    7. M. Socorro García-Cascales & M. Teresa Lamata & J. Miguel Sánchez-Lozano, 2012. "Evaluation of photovoltaic cells in a multi-criteria decision making process," Annals of Operations Research, Springer, vol. 199(1), pages 373-391, October.
    8. Daniels, Richard L. & Keller, L. Robin, 1992. "Choice-based assessment of utility functions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 52(3), pages 524-543, August.
    9. Dong-Ling Xu, 2012. "An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 195(1), pages 163-187, May.
    10. Belaïd Aouni & Cinzia Colapinto & Davide Torre, 2013. "A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making," Annals of Operations Research, Springer, vol. 205(1), pages 77-88, May.
    11. Keeney,Ralph L. & Raiffa,Howard, 1993. "Decisions with Multiple Objectives," Cambridge Books, Cambridge University Press, number 9780521438834, September.
    12. Williams, Michael L. & Dennis, Alan R. & Stam, Antonie & Aronson, Jay E., 2007. "The impact of DSS use and information load on errors and decision quality," European Journal of Operational Research, Elsevier, vol. 176(1), pages 468-481, January.
    13. James S. Dyer & Jianmin Jia, 1998. "Preference conditions for utility models:A risk-value perspective," Annals of Operations Research, Springer, vol. 80(0), pages 167-182, January.
    14. Kainuma, Yasutaka & Tawara, Nobuhiko, 2006. "A multiple attribute utility theory approach to lean and green supply chain management," International Journal of Production Economics, Elsevier, vol. 101(1), pages 99-108, May.
    15. B. Ahn & S. Choi, 2012. "Aggregation of ordinal data using ordered weighted averaging operator weights," Annals of Operations Research, Springer, vol. 201(1), pages 1-16, December.
    16. Guo, Min & Yang, Jian-Bo & Chin, Kwai-Sang & Wang, Hongwei, 2007. "Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1294-1312, November.
    17. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling, 2006. "Environmental impact assessment using the evidential reasoning approach," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1885-1913, November.
    18. Dan Horsky & M. R. Rao, 1984. "Estimation of Attribute Weights from Preference Comparisons," Management Science, INFORMS, vol. 30(7), pages 801-822, July.
    19. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    20. Bottomley, Paul A. & Doyle, John R., 2001. "A comparison of three weight elicitation methods: good, better, and best," Omega, Elsevier, vol. 29(6), pages 553-560, December.
    21. Wang, Ying-Ming & Chin, Kwai-Sang, 2011. "The use of OWA operator weights for cross-efficiency aggregation," Omega, Elsevier, vol. 39(5), pages 493-503, October.
    22. Yang, Jian-Bo, 2001. "Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties," European Journal of Operational Research, Elsevier, vol. 131(1), pages 31-61, May.
    23. Ali E. Abbas, 2006. "Maximum Entropy Utility," Operations Research, INFORMS, vol. 54(2), pages 277-290, April.
    24. Horowitz, I. & Zappe, C., 1995. "The linear programming alternative to policy capturing for eliciting criteria weights in the performance appraisal process," Omega, Elsevier, vol. 23(6), pages 667-676, December.
    25. Jiamin Wang, 2012. "Robust optimization analysis for multiple attribute decision making problems with imprecise information," Annals of Operations Research, Springer, vol. 197(1), pages 109-122, August.
    26. John C. Hershey & Paul J. H. Schoemaker, 1985. "Probability Versus Certainty Equivalence Methods in Utility Measurement: Are they Equivalent?," Management Science, INFORMS, vol. 31(10), pages 1213-1231, October.
    27. Yang, J.B. & Wang, Y.M. & Xu, D.L. & Chin, K.S., 2006. "The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties," European Journal of Operational Research, Elsevier, vol. 171(1), pages 309-343, May.
    28. Ananda, Jayanath & Herath, Gamini, 2005. "Evaluating public risk preferences in forest land-use choices using multi-attribute utility theory," Ecological Economics, Elsevier, vol. 55(3), pages 408-419, November.
    29. Peter H. Farquhar, 1984. "State of the Art---Utility Assessment Methods," Management Science, INFORMS, vol. 30(11), pages 1283-1300, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    2. Tim Chen & Hendri Daleanu & Chi-Huey Wong* & J.C.-Y. Chen, 2019. "Mathematical Derives of Evolutionary Algorithms for Multiple Criteria Decision Making," Sumerianz Journal of Scientific Research, Sumerianz Publication, vol. 2(1), pages 5-11, 01-2019.
    3. Chao Sun & Shiying Li & Yong Deng, 2020. "Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment," Mathematics, MDPI, vol. 8(2), pages 1-15, February.
    4. Chao Fu & Min Xue & Wenjun Chang, 2022. "Multiple criteria decision making with reliability of assessment," Annals of Operations Research, Springer, vol. 312(1), pages 121-157, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fu, Chao & Yang, Shan-Lin, 2010. "The group consensus based evidential reasoning approach for multiple attributive group decision analysis," European Journal of Operational Research, Elsevier, vol. 206(3), pages 601-608, November.
    2. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    3. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    4. Yang, Guo-liang & Yang, Jian-Bo & Xu, Dong-Ling & Khoveyni, Mohammad, 2017. "A three-stage hybrid approach for weight assignment in MADM," Omega, Elsevier, vol. 71(C), pages 93-105.
    5. 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.
    6. Fu, Chao & Yang, Jian-Bo & Yang, Shan-Lin, 2015. "A group evidential reasoning approach based on expert reliability," European Journal of Operational Research, Elsevier, vol. 246(3), pages 886-893.
    7. Zhang, Mei-Jing & Wang, Ying-Ming & Li, Ling-Hui & Chen, Sheng-Qun, 2017. "A general evidential reasoning algorithm for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1005-1015.
    8. Liu, Jiapeng & Liao, Xiuwu & Yang, Jian-bo, 2015. "A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty," European Journal of Operational Research, Elsevier, vol. 246(3), pages 858-873.
    9. J-B Yang & D-L Xu & X Xie & A K Maddulapalli, 2011. "Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1638-1654, September.
    10. Xiaojiao Qiao & Dan Shi, 2019. "Risk Analysis of Emergency Based on Fuzzy Evidential Reasoning," Complexity, Hindawi, vol. 2019, pages 1-10, November.
    11. Guo, Min & Yang, Jian-Bo & Chin, Kwai-Sang & Wang, Hongwei, 2007. "Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1294-1312, November.
    12. Dong, Yucheng & Liu, Yating & Liang, Haiming & Chiclana, Francisco & Herrera-Viedma, Enrique, 2018. "Strategic weight manipulation in multiple attribute decision making," Omega, Elsevier, vol. 75(C), pages 154-164.
    13. Maddulapalli, Anil Kumar & Yang, Jian-Bo & Xu, Dong-Ling, 2012. "Estimation, modeling, and aggregation of missing survey data for prioritizing customer voices," European Journal of Operational Research, Elsevier, vol. 220(3), pages 762-776.
    14. Chao Fu & Min Xue & Wenjun Chang, 2022. "Multiple criteria decision making with reliability of assessment," Annals of Operations Research, Springer, vol. 312(1), pages 121-157, May.
    15. Dong-Ling Xu, 2012. "An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 195(1), pages 163-187, May.
    16. Fu, Chao & Yang, Shanlin, 2011. "An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context," European Journal of Operational Research, Elsevier, vol. 212(1), pages 179-189, July.
    17. Yang, Guo-liang & Yang, Jian-bo & Liu, Wen-bin & Li, Xiao-xuan, 2013. "Cross-efficiency aggregation in DEA models using the evidential-reasoning approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 393-404.
    18. Ewa Roszkowska, 2020. "The extention rank ordering criteria weighting methods in fuzzy enviroment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(2), pages 91-114.
    19. Wen-Tao Guo & Van-Nam Huynh & Songsak Sriboonchitta, 2017. "A proportional linguistic distribution based model for multiple attribute decision making under linguistic uncertainty," Annals of Operations Research, Springer, vol. 256(2), pages 305-328, September.
    20. Guilan Kong & Lili Jiang & Xiaofeng Yin & Tianbing Wang & Dong-Ling Xu & Jian-Bo Yang & Yonghua Hu, 2018. "Combining principal component analysis and the evidential reasoning approach for healthcare quality assessment," Annals of Operations Research, Springer, vol. 271(2), pages 679-699, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:245:y:2016:i:1:d:10.1007_s10479-014-1657-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.