Ranking Approach to Scheduling Repairs of a Water Distribution System for the Post-Disaster Response and Restoration Service
Bibliographic record
Abstract
On the maintenance task list of each water distribution system (WDS) operator, determination of the order of undertaken repairs seems quite a typical task. Characteristics of damages, their localization, and other factors that influence repair sequencing have a sound impact on the execution of such tasks. In the case of the most complex cases where numerous failures of different types occur at the very same time (i.e., due to earthquakes), there is a long list of selection criteria that have to be analyzed to deliver an objectively logical schedule for repair teams. In this article, authors attempt to find out if it is possible to define pipe rankings in having obtained the best factors for defined objective functions (criteria), making it feasible to deliver judicious repair sequencing. For the purposes of this paper, a survey has been carried out. Its conclusions made it possible to propose a method to create rankings of pipes and evaluate them using a selected multicriteria decision method: preference ranking organization method for enrichment evaluation (PROMETHEE). The work was carried out for five different disaster scenarios that had been supplied by ‘The Battle of Post-Disaster Response and Restoration’ organization committee. Obtained results might be further used to finetune this sequencing method of undertaken repairs, while conclusions could be useful to model similar events in WDS when required. This article is an extended paper based on the conference preprint presented at the 1st International Water Distribution Systems Analysis (WDSA)/International Computing & Control for the Water Industry (CCWI) Joint Conference in July 23–25, 2018 in Kingston, Ontario, Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".