Main Article Content
Purpose of study: The current paper is the based on mathematical model of the job evolution system.
Methodology: The proposed method is the fusion of quadratic programming and fuzzy logic where quadratic programming is used to optimize objective function with related constraints in the form of non-linear formulation. Fuzzy logic is used to control uncertainty related information by estimating imprecise parameters
Main Finding: The optimal solution of the job evaluation based on fuzzy environment where goal is imprecise.
Application of this study: It is used in the areas where information is not exact.
The originality of this study: The novelty of the method is the fusion of quadratic programming and fuzzy logic.
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Authors retain the copyright without restrictions for their published content in this journal. IJSRTM is a SHERPA ROMEO Journal.
- Ammar, E. (2009). On fuzzy random multi objective quadratic programming. European Journal of Operational Research, 193(2), 329-341. https://doi.org/10.1016/j.ejor.2007.11.031
- Bellman, R.E. and Zadeh, L.A.(1970). Management Science, ser. B. 17, 141-164. https://doi.org/10.1287/mnsc.17.4.B141
- Bing –Yuan, C. (1993). Fuzzy Quadratic programming. Fuzzy sets and Systems, 53, 135 – 153. https://doi.org/10.1016/0165-0114(93)90168-H
- Chakraborty, M., Dubey, O. P.(2001). Goal Programming with Quadratic Preferences – An iterative Approach. International Journal of Management and System, 17(01), 25-34.
- Das, S. K., & Tripathi, S. (2020). A Nonlinear Strategy Management Approach in Software-Defined Ad hoc Network. Design Frameworks for Wireless Networks, 321-346. Springer, Singapore. https://doi.org/10.1007/978-981-13-9574-1_14
- Das, S. K., Kumar, A., Das, B., & Burnwal, A. P. (2013). On soft computing techniques in various areas. Computer Science & Information Technology (CS & IT), 3, 59-68. https://doi.org/10.5121/csit.2013.3206
- Das, S. K., Samanta, S., Dey, N., & Kumar, R. (2020). Design frameworks for wireless networks. Springer, Singapore. https://doi.org/10.1007/978-981-13-9574-1
- Das, S. K., Tripathi, S., & Burnwal, A. P. (2015a). Fuzzy based energy efficient multicast routing for ad-hoc network. In Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1-5. IEEE. https://doi.org/10.1109/C3IT.2015.7060126
- Das, S. K., Tripathi, S., & Burnwal, A. P. (2015b). Intelligent energy competency multipath routing in wanet. In Information systems design and intelligent applications, pp. 535-543. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_53
- Das, S. K., Tripathi, S., & Burnwal, A. P. (2015c). Design of fuzzy based intelligent energy efficient routing protocol for wanet. In Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1-4. IEEE. https://doi.org/10.1109/C3IT.2015.7060201
- De, D., Mukherjee, A., Das, S. K., & Dey, N. (2020). Nature Inspired Computing for Wireless Sensor Networks. Springer. https://doi.org/10.1007/978-981-15-2125-6
- Hannanm, E. L. (1981). Linear programming with multiple fuzzy goals. Fuzzy sets and systems, 6 (3), 235-248. https://doi.org/10.1016/0165-0114(81)90002-6
- Leep, T. L. and Michael D. Crino (1990). Personnel/ Human Resource Management, Macmillan, New York.
- Milkovich, G.T. and Boudreau, W. (1990). John Personnel/ Human Resource Management (diagnostic approach), 5th ed.
- Mukherjee, Burnwal, A.P. and Singh, D. (2000). Fuzzy Geometric Programming using additive operative, News bull. Cal. Math. Society 23 (5&6), P.20-24.
- Tiwari, R., Dharmar, S., Rao, J. (1987). Fuzzy goal programming an additive model. Fuzzy sets and systems, 24(1), 27-34. https://doi.org/10.1016/0165-0114(87)90111-4
- Zimmermann, H.J. (1978). Fuzzy programming and linear programming with general objective functions. Fuzzy sets and systems, 1, pp.45. https://doi.org/10.1016/0165-0114(78)90031-3