Intuitionistic z-number and application for fuzzy risk assessment in oil and gas industry
The nature of decision-making in daily life is highly characterized by uncertainty and vagueness. Due to the lack of information, decision makers make subjective conclusions that are always partially reliable. Intuitionistic Z-numbers (IZN) are the generalization of fuzzy Z-numbers, which are powerf...
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| Format: | Thesis |
| Language: | en |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46844/1/Intuitionistic%20z-number%20and%20application%20for%20fuzzy%20risk%20assessment%20in%20oil%20and%20gas%20industry.pdf https://umpir.ump.edu.my/id/eprint/46844/ |
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| Summary: | The nature of decision-making in daily life is highly characterized by uncertainty and vagueness. Due to the lack of information, decision makers make subjective conclusions that are always partially reliable. Intuitionistic Z-numbers (IZN) are the generalization of fuzzy Z-numbers, which are powerful in handling uncertainty and partially reliable information by considering the level of sureness when obtaining the decision information. This study defines the arithmetic operations of IZN based on the horizontal membership function to guide the aggregation of decision-making information from multiple decision makers. The magnitude and distance measure of the IZN are also defined, and their properties are studied. Next, a novel ranking function of the IZN based on the vectorial distance and spread is proposed to compare the ranking of IZN. By using the proposed IZN, the multi-criteria decision-making (MCDM) model is developed, namely the integrated analytic hierarchy process (AHP) and the technique for order of preferences by similarity to ideal solutions (TOPSIS). The model is developed for catering to the decision information in the form of trapezoidal intuitionistic fuzzy numbers and Z-numbers. The integrated model is then generalized to process the decision information in the form of IZN. The proposed models are also implemented to perform risk ranking in the oil and gas industry. Accordingly, the newly developed AHP-TOPSIS model is developed for prioritizing hazards via Hazard and Operability (HAZOP). The sensitivity analysis is performed to validate the consistency of the hazard ranking by increasing the criteria weights up to 50%. The results have shown that the fuzzy AHP-TOPSIS model based on the IZN gives the best performance compared to other forms of fuzzy numbers and existing fuzzy MCDM models. |
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