Single-objective deterministic versus multi-objective stochastic water network design: Practical considerations for the water industry
Bibliographic record
Abstract
The strategy in single-objective deterministic water network design is to size and locate components to minimize capital cost and meet future peak demands at or above a minimum pressure. Increasingly, practitioners are turning to multi-objective stochastic design to balance the minimum-cost objective with hydraulic performance objectives. The aim of the current paper is to review single-objective deterministic and multi-objective stochastic network design and discuss practical considerations concerning their advantages and disadvantages of relevance to water industry professionals and practitioners. Key differences in data and computational requirements, comprehensiveness of analysis, and decision flexibility between the two approaches are illustrated with a complex, hypothetical network example. A Monte-Carlo simulation program was used to solve the multi-objective stochastic problem and generate a set of Pareto or near-Pareto solutions with pipe cost ranging between $9.3 and $17.4 million and hydraulic robustness ranging between 65.8 and 96.4 per cent. Results indicated a non-linear relationship between pipe cost and robustness typical of many systems and that a large premium must be paid to achieve marginal improvements in robustness beyond a value of 90 per cent. The MCS program was run for 30.6 h to test the hypothetical network against a broad range of demands to ensure a high level of hydraulic robustness. The Pareto curve allows the decision maker the opportunity to quickly assess trade-offs between pipe cost and robustness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".