OPTIMIZING PRODUCTIVITY RATE TO MINIMIZE OVERDRAFT INTEREST PAYMENT IN INFRASTRUCTURE CONSTRUCTION PROJECTS
Bibliographic record
Abstract
Winning a bid is a good opportunity for contractors that includes risks. After winning a project, contractors typically receive payments after two-three months of work completion that leads to negative cash flow (overdraft) throughout the duration of a project. Hence, contractors borrow from banks and pay monthly interests on the amount of overdraft they owe. To solve this problem, a hybrid model utilizing Discrete Event Simulation using SIMPHONY software, a special purpose simulation tool developed by the University of Alberta, accompanied by Markov Chain prediction technique. The developed hybrid model allows contractors to test different scenarios in search of the optimum productivity rate and payment arrangement to minimize negative cash flow. A case study utilizing a typical road construction project is used to test and validate the developed model and its ability to determine the optimum scenario. Results revealed that markup percentage and initial investment are two crucial factors to deliver the project successfully. In the harsh market, increasing the amount of cash to invest without a reasonable markup (at least 10%) will no longer make a profit. But, if the markup percentage could be increased by more than 15%, it will offer a chance to the contractor to make a profit and successfully deliver the project with initial investment reasonably low; and save a flexible productivity rate to finish the project within the schedule.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".