Analyzing the Lean Principles in Integrated Planning and Scheduling Methods
Bibliographic record
Abstract
The shortcomings and limitations of conventional planning and scheduling methods led to a great deal of emphasis on combining them and developing integrated scheduling methods.Also, lean principles and tools are included in the integrated scheduling methods' structure to develop more effective scheduling strategies.This paper implements a multi-step methodology to identify and analyze the lean principles utilized in integrated scheduling methods.The findings show that integrated scheduling methods, Building Information Modelling (BIM)-Last Planner System (LPS)-Kanban, BIM-LPS, Location-based Management System (LBMS)-LPS-CPM, and BIM-LBMS have included a variety of lean principles into their frameworks.Moreover, improving the reliability of the planning, increasing transparency, identifying and eliminating waste, detecting and solving spatiotemporal conflict, enabling the coordination of the lookahead plans, and continuous flow of work have received the most attention in the integrated scheduling methods.This paper contributes significantly to the body of knowledge by raising project stakeholders' awareness of the lean principles utilized in integrated scheduling methods in construction 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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".