Development of an Optimization Model Based on Business Process Re-Engineering to Minimize Construction Projects Delay
Bibliographic record
Abstract
Related decisions can affect project scheduling in a construction supply chain (CSC).After all, the project activities require vital resources and collaboration among project stakeholders.That effects can occur negatively, such as delay, budget overrun, and project performance.These effects are considered wastes in lean construction (LC).The concept of LC is still limited regarding application in CSC.This study aims to develop a decision-making model (LC tool) to minimize project delays using a mixed integer linear programming optimization model.The proposed model is triggered by the business process re-engineering of the scheduling process.A construction company case example that delivers construction renovation projects to its customers is considered for validation.This approach is applied in two stages.In the first stage, the information process flow of the company is developed to derive the inputs required for the logistics and scheduling optimization model.Then in the second stage, the mathematical model is developed based on the inputs to generate optimal supplier selection, projects schedules, and resource utilization decisions.By using the proposed LC tool, the results show that delays in multiple projects can be minimized.Finally, decision-makers can use this technique to manage concurrent projects and suppliers that leanly provide essential resources to these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".