Enhanced heuristic for finance-based scheduling of construction projects
Bibliographic record
Abstract
Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".