Comparison of defuzzification methods for fuzzy stochastic linear programming
Also available in printed version
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| Other Authors: | |
| Format: | Master's thesis |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/104288 |
| Abstract | Abstract here |
| _version_ | 1854934115384557568 |
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| author | Gan, Siew Ling |
| author2 | Zaitul Marlizawati, supervisor |
| author_facet | Zaitul Marlizawati, supervisor Gan, Siew Ling |
| author_sort | Gan, Siew Ling |
| description | Also available in printed version |
| format | Master's thesis |
| id | utm-123456789-104288 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1042882025-08-21T01:21:15Z Comparison of defuzzification methods for fuzzy stochastic linear programming Gan, Siew Ling Zaitul Marlizawati, supervisor Science Also available in printed version The present study focused on comparison of three defuzzification methods in transforming fuzzy two-stage stochastic linear programming problem into a crisp problem. The fuzzy transformation techniques that utilized in this study were Yager’s robust ranking method, generalized mean integration representation (GMIR) method, and centroid defuzzification method (CDM). Besides that, an assumption that the probability distribution obtained via expert was fuzzy and consisted only partial information was made. Five problems which modified based on Dakota’s Furniture Company were presented to give an illustration on how the fuzzy transformations using the three mentioned techniques were carried out. The defuzzified two-stage stochastic linear programming problems from each of the techniques were solved using a modelling system of GAMS, which implemented using a solver called DECIS. The difference between first problem and the rest of the problems was demand levels in first problem were symmetric triangular fuzzy numbers. Transformation of first problem using three different techniques resulted in getting the same model formulation, and hence the result obtained from GAMS/DECIS obviously was similar. The results of Problem 2 and Problem 3 obtained from the GAMS/DECIS showed a slight difference in resource quantities, production quantities, and the total profit, and CDM method showed the best optimum solutions. Meanwhile, GMIR method showed better optimum solutions in Problem 4 and 5. Hence, it can be concluded that CDM and GMIR are best methods of defuzzification for non-symmetric triangular fuzzy numbers problems comparing to Yager’s robust ranking method atiff UTM 182 p. Thesis (Sarjana Sains (Matematik)) - Universiti Teknologi Malaysia, 2014 2025-04-10T07:16:59Z 2025-04-10T07:16:59Z 2014 Master's thesis https://utmik.utm.my/handle/123456789/104288 valet-20160327-122036 vital:85375 Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Science Gan, Siew Ling Comparison of defuzzification methods for fuzzy stochastic linear programming |
| thesis_level |
Master
|
| title | Comparison of defuzzification methods for fuzzy stochastic linear programming |
| title_full | Comparison of defuzzification methods for fuzzy stochastic linear programming |
| title_fullStr | Comparison of defuzzification methods for fuzzy stochastic linear programming |
| title_full_unstemmed | Comparison of defuzzification methods for fuzzy stochastic linear programming |
| title_short | Comparison of defuzzification methods for fuzzy stochastic linear programming |
| title_sort | comparison of defuzzification methods for fuzzy stochastic linear programming |
| topic | Science |
| url | https://utmik.utm.my/handle/123456789/104288 |
| work_keys_str_mv | AT gansiewling comparisonofdefuzzificationmethodsforfuzzystochasticlinearprogramming |