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Manuscript ID : 537005 Visit : 122 Page: 63 - 80

Article Type: Original Research

A Framework for Dynamic MCDM in Fuzzy Environment (Case Study: Emergency Department and Triage Patients)

Subject Areas : Industrial Management

alireza alinezhad 1 (Associate Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.)

Received: 2016-05-18 Accepted : 2016-11-09 Published : 2016-12-10

Keywords:

Abstract :

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value. However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Dynamic decisions arise in many applications including military, medical, management, sports and emergency situations. This study proposes a flexible framework for dynamic MCDM, based on the concept of fuzzy sets theory and the VIKOR method to provide a rational, scientific and systematic process for prioritizing patients in Emergency Department (ED) under a fuzzy environment where the uncertainty, subjectivity, and vagueness are addressed with linguistic variables parameterized by triangular fuzzy numbers.

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_||_

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    2.
  2. Agrawal, Rakesh, & Srikant, Ramakrishnan. (1994). Fast Algorithms for Mining Association Rules in Large Databases. Paper presented at the Proceedings of the 20th International Conference on Very Large Data Bases.
    3.
  3. Amirteimoori, Alireza. (2007). DEA efficiency analysis: Efficient and anti-efficient frontier. Applied Mathematics and Computation, 186(1), 10-16. doi: http://dx.doi.org/10.1016/j.amc.2006.07.006
    4.
  4. Amirteimoori, Alireza, Emrouznejad, Ali, & Khoshandam, Leila. (2013). Classifying flexible measures in data envelopment analysis: A slack-based measure. Measurement, 46(10), 4100-4107. doi: http://dx.doi.org/10.1016/j.measurement.2013.08.019
    5.
  5. Amirteimoori, Alireza, Kordrostami, Sohrab, & Azizi, Hossein. (2016). Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D: Transport and Environment, 48, 411-424. doi: 10.1016/j.trd.2015.12.016
    6.
  6. Archak, Nikolay, Ghose, Anindya, & Ipeirotis, Panagiotis G. (2011). Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 57(8), 1485-1509. doi: 10.1287/mnsc.1110.1370
    7.
  7. Azizi, Hossein. (2011). The interval efficiency based on the optimistic and pessimistic points of view. Applied Mathematical Modelling, 35(5), 2384-2393. doi: http://dx.doi.org/10.1016/j.apm.2010.11.055
    8.
  8. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi: doi:10.1287/mnsc.30.9.1078
    9.
  9. Bellazzi, Riccardo, & Zupan, Blaz. (2008). Predictive data mining in clinical medicine: Current issues and guidelines. International Journal of Medical Informatics, 77(2), 81-97. doi: http://dx.doi.org/10.1016/j.ijmedinf.2006.11.006
    10.
  10. Breese, John S., Heckerman, David, & Kadie, Carl. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Madison, Wisconsin.
    11.
  11. Camanho, A. S., & Dyson, R. G. (2005). Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. European Journal of Operational Research, 161(2), 432-446. doi: http://dx.doi.org/10.1016/j.ejor.2003.07.018
    12.
  12. Cao, Xin, Cong, Gao, & Jensen, Christian S. (2010). Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment, 3(1-2), 1009-1020. doi: 10.14778/1920841.1920968
    13.
  13. Chadwick, Andrew, & May, Christopher. (2003). Interaction between States and Citizens in the Age of the Internet: "e-Government" in the United States, Britain, and the European Union. Governance, 16(2), 271-300. doi: 10.1111/1468-0491.00216
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  14. Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3-4), 181-186. doi: 10.1002/nav.3800090303
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  15. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi: http://dx.doi.org/10.1016/0377-2217(78)90138-8
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  17. Chen, Hsinchun, Chung, Wingyan, Qin, Yi, Chau, Michael, Xu, Jennifer Jie, Wang, Gang, . . . Atabakhsh, Homa. (2003). Crime data mining: an overview and case studies. Paper presented at the Proceedings of the 2003 annual national conference on Digital government research, Boston, MA, USA.
    18.
  18. Chen, Min. (2013). Towards smart city: M2M communications with software agent intelligence. Multimedia Tools and Applications, 67(1), 167-178. doi: 10.1007/s11042-012-1013-4
    19.
  19. Chen, Min. (2014). NDNC-BAN: Supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks. Information Sciences, 284, 142-156. doi: http://dx.doi.org/10.1016/j.ins.2014.06.023
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