Schedule compression and emerging waste in construction: an assessment of overlapping activities
Bibliographic record
Abstract
Purpose Compressing the schedule by using overlapping activities is a commonly adopted approach for accelerating projects. However, this approach might channel a variety of risks into the construction processes. Risks imply waste; still, evaluating the effects of using overlapping activities on schedule quality has been a looming gap in construction research. Therefore, this paper aims to study the quality of overlapping in terms of emerging waste and to demarcate the boundaries of the overlapping envelope. Design/methodology/approach This study presents a method for assessing the consequences of implementing overlapping activities in a schedule on two types of waste namely waiting time and variation gap. A critical path method (CPM) network including eleven activities is modeled stochastically where the durations of individual activities are sampled as beta-distributions. Using program evaluation and review technique (PERT) assumptions to calculate the schedule dates, the network is simulated for various amounts of overlapping and the corresponding waste is quantified each time. Findings Results show that not only the returns on overlapping are diminishing after a certain overlap percentage, but also waste in the production system increases. Particularly, results reveal that compressing the schedule leads to a decrease in variation gaps, but at the same time, it leads to a larger increase in waiting times, which creates more waste. Originality/value The presented study shows through simulation how overlapping activities affects productivity by identifying wastes. It shows that despite the apparent gains, overlaps should be used with caution, and while considering the side-effects of increased waste which introduces a need for increased managerial awareness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".