Time–cost combined optimization in planning infrastructure construction projects under environment induced time‐window constraints
Bibliographic record
Abstract
To accommodate environment-induced time-window constraints, environment-sensitive activities are arranged within allowable time windows while maintaining technological precedence and other logical relationships on the project. This research advances classical time–cost trade-off (TCT) analysis by incorporating time-window constraints in project planning, creating a more complex optimization problem that becomes computationally prohibitive for real-world applications. To overcome this challenge, an integrated project planning framework combining project time and cost into a single objective function is formalized. The optimization solution employs time-window scheduling algorithms to simulate method combinations through enumerated simulation. A reward function is defined to evaluate alternatives based on their impact on project cost and duration. In addition, a sample size reduction technique is utilized to maintain computational efficiency of random sampling without sacrificing accuracy. The methodology's practical application is demonstrated through a case study of a river-crossing bridge project in remote northern Canada, which is planned to validate its effectiveness in planning real-world infrastructure projects under stringent environment-induced time-window constraints.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".