Integrating critical chain project management with last planner system for linear scheduling of modular construction
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a new method for project tracking and control of integrated offsite and onsite activities in modular construction considering practical characteristics associated with this type of construction. Design/methodology/approach The design embraces building information modelling and integrates last planner system (LPS), linear scheduling method (LSM) and critical chain project management (CCPM) to develop tracking and control procedures for modular construction projects. The developed method accounts for constraints of resources continuity and uncertainties associated with activity duration. Features of proposed method are illustrated in a case example for tracking and control of modular projects. Findings Comparison between developed schedule and Monte Carlo simulation showed that baseline duration generated from simulation exceeds that produced by developed method by 12% and 10% for schedules with 50% and 90% confidence level, respectively. These percentages decrease based on interventions of members of project team in the LPS sessions. The case example results indicate that project is delayed 5% and experienced cost overrun of 2.5%. Originality/value Developed method integrated LPS, LSM and CCPM while using metrics for reliability assessment of linear schedules, namely, critical percent plan complete (PPC cr ) and buffer index (BI). PPC cr and BI measure percentage of plan completion for critical activities and buffer consumption, respectively. The developed method provides a systematic procedure for forecasting look-ahead schedules using forecasting correction factor Δ t and a newly developed tracking and control procedure that uses PPC cr and BI. Quantitative cost analysis is also provided to forecast and monitor project costs to prove the robustness of proposed framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".