Optimizing planning of build–operate–transfer projects to maximize investor profit
Bibliographic record
Abstract
During planning of repetitive build–operate–transfer (BOT) construction projects, investors and (or) investor-representatives are always faced with a challenging task to identify an optimal project plan that maximizes the investor’s profit within a specified concession period. Minimizing project duration in BOT projects often results in additional costs, however, it increases the project operation period and accordingly increases the investor’s profit. Optimal plan of constructing repetitive BOT projects requires identifying construction methods and the associated start and finish time of each construction activity according to the availability of resources and contractors. Traditional scheduling methods such as critical path method (CPM) networks do not provide efficient planning of repetitive construction projects as they (1) do not maintain crew work continuity as well as resources availability, and (2) often result in large and repetitive number of activities to model repetitive construction projects. This paper presents the development of an optimization model using linear scheduling method that identifies the near-optimal plan for constructing repetitive BOT projects to maximize the investor’s profit. The development of this optimization model includes three steps: formulation phase that formulates the model decision variables, objective function, and constraints; implementation phase that executes the model computations using genetic algorithms and identifies the model inputs and outputs; and evaluation phase that verifies the model performance and document its value. A case study of a construction project is presented to illustrate the new capabilities of the model. The results of the model showed 21% profit increase as compared to the existing schedule adopted by the investor.
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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 | 0.001 |
| 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 | 0.001 | 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".