Pull planning versus push planning: Investigating impacts on crew performance from a location-based perspective
Bibliographic record
Abstract
Push planning and pull planning are different approaches used for production planning and control. Push planning uses predetermined dates to control a project, whereas pull planning utilizes the system’s current state. Although researchers have compared these approaches from production planning perspective to improve project performance, the differences between push and pull in construction and their impacts on crew performance and congestion have not yet been explored. Therefore, this research aims to investigate the underlying mechanisms of applying pull and push approaches at the location level through simulation, in addition to proposing hypotheses relating push and pull approaches to project and crew performance metrics. Agent-based modeling is used to simulate and describe how push and pull approaches affect crew performance. Results show that pull approaches can achieve significantly higher productivity, less idle time, lower crew turnover, and fewer task interruptions, although they can result in slightly increased project durations. Cross-analyzing the mentioned results with other performance metrics reveals that push and pull approaches should be applied together to achieve a flexible production control system. The significance of this study is embedded in exploring and understanding how the choice of push and pull planning approaches impacts the location-based management of tasks and crew performance. Such impacts on productivity, crew performance, and the flow of site operations enable a convergence to generalized conclusions regarding the efficacy of each method.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".