Role of System Resilience in Dealing with Threats Using an Entropy-Based TOPSIS Approach: A Case Study in an Oil Products Distribution Company
Bibliographic record
Abstract
Objectives: The current study aimed at assessing the ability of system resilience against threats using an integrated method based on entropy and technique for order of preference by similarity to ideal solution (TOPSIS) in an oil company. Methods: The threats were identified through field observation, literature review, and expert opinion in the industry. Afterward, the required data were gathered, and the resilience status was examined using three structured questionnaires for each category of the threats. The weights of resilience criteria computed for each group of the threats using entropy, and were then ranked through the TOPSIS method. Results: Learning (0.34) and anticipating (0.15) had the highest and lowest impacts on the category of regular threats, respectively. In the case of irregular threats, anticipating (0.31) and monitoring (0.21) had the highest and lowest impacts, respectively. As for unexampled threats, learning and anticipating (0.26) had the highest impact, and responding (0.23) had the lowest impact. The results of TOPSIS analysis indicated that regular threats, irregular threats, and unexampled threats were ranked in the first, second, and third positions with scores of 0.52, 0.48, and 0.46, respectively. Conclusions: To ameliorate resilience in complicated systems, managers should strengthen RE-related indicators along with working on the indicators which are in poor condition. The findings of this study can be used by managers and decision-makers to identify system weaknesses and improve comprehensive technical and applied plans.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".