Using Fuzzy Preference Relations to Predict the Success Possibility of Implementing the Oceanographic & Meteorologic Integration Orchestrator

Abstract:

This study proposes an analytic hierarchical prediction model based on consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the implementation oceanographic & meteorologic Integration Orchestrator. Pairwise comparisons are used to determine the priority weights of influential factors and the ratings of success or failure outcomes amongst decision makers. The subjectivity and vagueness in the prediction procedures are dealt with using linguistic terms quantified in an interval scale [0,1]. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate knowledge management, inhibit adoption or take remedial actions to increase the possibility of successful oceanographic & meteorologic initiatives. This proposed approach is demonstrated with a real case study involving seven influential factors assessed by eleven evaluators solicited from a semiconductor engineering incorporation located in Taiwan.

• Periodical: Applied Mechanics and Materials (Volumes 52-54)
• Pages: 1812-1817
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.52-54.1812
• Online since: March 2011
• Authors: Wen Ting Chen, Ching Tien Shih, J.C. Tsai
• Keywords: Analysis Hierarchy Process (AHP), Decision-Making, Meteorologic, Oceanographic, Preference Relations

References

[1] Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, J. G. Powers, 2005：A description of the advanced research WRF version 2. NCAR/TN-468+STR, National Center for Atmospheric Research technical note, 88pp.

[2] Zhu, M. and B. W. Atkinson, 2005: Simulated Climatology of Atmospheric Ducts over the Persian Gulf. Bound. -Layer Meteor., 15, 433–452.

DOI: 10.1007/s10546-004-1428-1

[3] Herrera-Viedma, et al., Some issues on consistency of fuzzy preference relations, European Journal of Operational Research, vol. 154, pp.98-109, (2004).

DOI: 10.1016/s0377-2217(02)00725-7

[4] F. Chiclana, et al., Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations, Fuzzy Sets and Systems, vol. 97, pp.33-48, (1998).

DOI: 10.1016/s0165-0114(96)00339-9

[5] F. Chiclana, et al., Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations, Fuzzy Sets and Systems, vol. 122, pp.277-291, (2001).

DOI: 10.1016/s0165-0114(00)00004-x

[6] F. Herrera and E. Herrera-Viedma, Choice functions and mechanisms for linguistic preference relations, European Journal of Operational Research, vol. 120, pp.144-161, (2000).

DOI: 10.1016/s0377-2217(98)00383-x

[7] F. Herrera and E. Herrera-Viedma, Linguistic decision analysis: steps for solving decision problems under linguistic information, Fuzzy Sets and Systems, vol. 115, pp.67-82, (2000).

DOI: 10.1016/s0165-0114(99)00024-x

[8] F. Herrera, et al., Multiperson decision-making based on multiplicative preference relations, European Journal of Operational Research, vol. 129, pp.372-385, (2001).

DOI: 10.1016/s0377-2217(99)00197-6

[9] F. Herrera, et al., A rational consensus model in group decision making using linguistic assessments, Fuzzy Sets and Systems, vol. 88, pp.31-49, (1997).

DOI: 10.1016/s0165-0114(96)00047-4

[10] Z. Xu, Incomplete linguistic preference relations and their fusion, Information Fusion, vol. 7, pp.331-337, (2006).

DOI: 10.1016/j.inffus.2005.01.003

[11] T. -C. Wang and Y. -H. Chen, Applying consistent fuzzy preference relations to partnership selection, Omega, vol. 35, pp.384-388, (2007).

DOI: 10.1016/j.omega.2005.07.007

[12] T. -C. Wang and T. -H. Chang, Forecasting the probability of successful knowledge management by consistent fuzzy preference relations, Expert Systems with Applications, vol. 32, pp.801-813, (2007).

DOI: 10.1016/j.eswa.2006.01.021