|
| A Personnel Selection Problem in Healthcare System Using Fuzzy-TOPSIS Approach |
| |
| View Full Text View/Add Comment Download reader |
| KeyWord:Fuzzy-TOPSIS, triangular fuzzy number, separation measure, closeness coefficient, ideal solution |
| Author Name | Affiliation | | Salman Khalil | Department of Community Medicine, Jawaharlal Nehru Medical College, A. M. U., Aligarh - 202 002, India | | Umar Muhammad Modibbo | Department of Statistics & Operations Research, Modibbo Adama University, P.M.B. 2076, Yola, Nigeria | | Ather Aziz Raina | Department of Mathematics, Government Degree College Thannamandi, (J&K) -185212, India | | Irfan Ali | Department of Statistics & Operations Research, Aligarh Muslim University, Aligarh-202002, India |
|
| Hits: 250 |
| Download times: 413 |
| Abstract: |
| The methods of multiple criteria decision-making (MCDM) are increasingly becoming the most desired tools for making daily decisions in various fields of human endeavors. Staff employment in any sector requires a thorough evaluation of the applicant before selection to ensure effective and efficient service delivery. Besides, healthcare is one of the most complicated organizations dealing with human lives. This paper has developed a staff selection model considering a fuzzy environment by using the technique for order preference similar to the ideal solution (TOPSIS) method. For the delivery and promotion of quality healthcare systems, medical staff selection is crucial to the system. Therefore, the study evaluates medical staff by using the expert's linguistic judgement under the criteria of skill, experience and ability to respond to a problem. The expert's vagueness in judgments has been represented by using fuzzy triangular numbers. The study determines the closeness coefficient, the measures of separation and the ideal solutions of the TOPSIS method. The most appropriate medical staff are ranked and selected based on the available criteria. The Fuzzy-TOPSIS method is simple and can help other organizations achieve proper ranking, evaluation and selection of qualified candidates, as it takes imprecise information into account. |
|
|
|
