<![CDATA[Contractor evaluation remains a major challenge in safety-critical industries such as oil and gas, where the need to comply with stringent Health, Safety, and Environment (HSE) standards demands a robust and objective assessment mechanism. The existing manual evaluation methods are plagued by subjectivity, inconsistent data handling, and inability to resolve performance ties, leading to unreliable contractor differentiation. To address this problem, this study investigates how can a computational decision support framework minimize subjectivity and enhance ranking precision in contractor evaluations. It proposes a Decision Support System (DSS) based on the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to improve the accuracy, transparency, and efficiency of evaluations within the Contractor Safety Management System (CSMS). The DSS integrates qualitative and quantitative criteria using fuzzy logic and expert-assigned linguistic weights. Developed following the Waterfall software development lifecycle, the system was validated using black box testing and applied to realistic simulated data from ten contractors evaluated across multiple criteria and subcriteria. Results demonstrate that the DSS effectively resolves score ties present in manual evaluations, enabling finer distinctions among contractors, with the highest closeness coefficient of 0.479 achieved by the top-ranked contractor. This value reflects a 47.9% closeness to the ideal performance profile, marking a significant improvement over binary or aggregate-based evaluation methods..User feedback confirmed high satisfaction with system usability and performance. The proposed DSS offers a robust and adaptable framework for contractor evaluation, enhancing decision-making accuracy and operational transparency in high-risk environments. Its novelty lies in the integration of fuzzy linguistic modeling within a CSMS context to operationalize HSE performance evaluations. Future research should focus on incorporating advanced fuzzy logic methods and artificial intelligence to facilitate real-time, dynamic contractor evaluations under uncertainty.]]>
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