IF‐EM: An interval‐parameter fuzzy linear programming model for environment‐oriented evacuation planning under uncertainty
Bibliographic record
Abstract
Abstract The processes and factors involved in evacuation activities are associated with a variety of uncertainties, posing major challenges to evacuation planners. This study represents an attempt to employ inexact optimization techniques for addressing uncertainties in evacuation practices. In this study, an interval‐parameter fuzzy evacuation management (IF‐EM) model is developed for supporting environment‐oriented evacuation management under uncertainty. Through IF‐EM, uncertainties in the model's stipulations and coefficients which are expressed as fuzzy sets and interval numbers can be directly communicated into the optimization process, greatly enhancing the robustness of the optimization system. The model is then applied to a case study and solved through a two‐step interactive algorithm. A number of evacuation schemes can be generated by adjusting decision variables within their solution intervals according to projected planning conditions, reflecting various decision policies and a compromise between system optimality and stability. The relationships among vehicle allocation pattern, evacuation time, system satisfaction level, and system reliability level can be effectively reflected, facilitating more in‐depth analyses of interactions among system efficiency, environmental protection, and economic cost. Results from the case study suggest that the proposed IF‐EM model is applicable to practical evacuation problems that are associated with uncertainties and complexities. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".