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    Multi-objective mathematical model based on fuzzy hybrid multi-criteria decision-making and FMEA approach for the risks of oil and gas projects
    (Emerald, 2020-10-28)
    Purpose: The purpose of this study is to provide a comprehensive framework for analyzing risk factors in oil and gas projects. Design/methodology/approach: This paper consists of several sections. In the first section, 19 common potential risks in the projects of Pars Oil and Gas Company were finalized in six groups using the Lawshe validation method. These factors were identified through previous literature review and interviews with experts. Then, using the “best-worst multi-criteria decision-making” method, the study measured the weights associated with the performance evaluation indicators of each risk. Consequently, failure mode and effects analysis (FMEA) and the grey relational analysis (GRA)-VIKOR mixed method were used to rank and determine the critical risks. Finally, to assign response strategies to each critical risk, a zero-one multi-objective mathematical programming model was proposed and developed Epsilon-constraint method was used to solve it. Findings: Given the typical constraints of projects which are time, cost and quality, of the projects that companies are often faced with, this study presents the identified risks of oil and gas projects to the managers of the oil and gas company in accordance with the priority given in the present research and the response to each risk is also suggested to be used by managers based on their organizational circumstances. Originality/value: This study aims at qualitative management of cost risks of oil and gas projects (case study of Pars Oil and Gas Company) by combining FMEA, best worst and GRA-VIKOR methods under fuzzy environment and Epsilon constraints. According to studies carried out in previous studies, the simultaneous management of quantitative and qualitative cost of risk of oil and gas projects in Iran has not been carried out and the combination of these methods has also been innovated.
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    Multi-criteria classification of spare parts in the steel industry
    (Associação Brasileira de Engenharia de Produção, 2025-01-22)
    Goal: This research addresses the critical challenge of evaluating spare parts inventory in the steel industry to enhance maintenance efficiency and reduce operational costs. Design/methodology/approach: The study applies the Analytic Hierarchy Process (AHP), a widely recognized multi-criteria decision-making (MCDM) method, to develop a robust decision support system. A hierarchical structure of criteria and sub-criteria, along with alternatives (spare parts), was constructed based on an extensive literature review and validated through input from three maintenance and inventory management experts. The system was implemented in a Brazilian steel plant. Results: The AHP-based framework systematically classified spare parts, emphasizing their criticality. Spare Parts 1 and 2 were categorized as Class B, scoring 0.6 and 0.56, while Spare Parts 3 and 4 were classified as Class A, scoring 0.82 and 0.83. These findings confirm the effectiveness of the AHP methodology in prioritizing spare parts for improved inventory management and decision-making. Sensitivity analysis validated the framework's robustness, demonstrating stable classifications across varying criteria weights. Limitations of the investigation: While tailored to a Brazilian steel plant, the framework's scalability is evident. Limitations include its reliance on a specific context and the involvement of a limited number of experts, suggesting opportunities for broader validation. Practical implications: The simplified AHP framework gives managers an accessible tool for classifying spare parts, eliminating the need for complex hybrid methods. It enables efficient decision-making, particularly in industries with high operational demands. Originality: This research contributes a novel multi-criteria decision-making model for spare parts classification, significantly advancing maintenance efficiency and cost-effectiveness compared to traditional single-criterion approaches.
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