A hierarchical assessment of resilience engineering indicators in petrochemical industries using AHP and TOPSIS
Bibliographic record
Abstract
Abstract Resilience engineering (RE) is a proactive approach that enables complex systems to deal with adverse events and improve safety management by enhancing structural and organizational capabilities. A methodological examination of RE‐related studies showed that they had only focused on some major indicators so that subindicators have been mostly neglected. This study aims to present a hierarchical analysis to identify the importance degree of indicators and subindicators of RE using analytic hierarchy process (AHP) in a petrochemical plant. To accomplish this, a pairwise comparison matrix of the indicators and subindicators was used to collect the data required for AHP approach. To demonstrate the applicability of the AHP results, this study ranks the units of the petrochemical plant using the technique for the order of preference by similarity to an ideal solution (TOPSIS) approach based on the importance degree of RE indicators. A questionnaire was used to gather data related to RE indicators so we could use the TOPSIS method. The results of the AHP showed that management commitment, buffering capacity, and reporting culture were the most influential RE indicators. In addition, anticipation had the lowest impact on RE. The most important subindicators of the RE indicators were also identified using a hierarchical analysis through AHP. The results of TOPSIS provided a best–worst analysis of the units of the petrochemical plant. The findings of this study could help safety managers formulate better‐targeted safety policies by investing in influential indicators and subindicators of RE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".