Illustrating the impact of implementing work zones, adjusting work patterns and optimizing crew starting positions to minimize spatial conflicts
Bibliographic record
Abstract
Purpose The construction industry frequently encounters problems with low productivity leading to delays in project schedules. This delay is often addressed by increasing manpower to perform adjacent tasks simultaneously. Although this approach can theoretically expedite project completion, it often results in spatial conflicts, as work teams must coordinate their movements within limited spaces. These conflicts cause congestion and reduced productivity, highlighting the need for better spatial planning strategies to align manpower increases with efficient crew movement. Design/methodology/approach This study employs simulation to investigate spatial management strategies for mitigating the negative effects of increased manpower. The analysis focuses on the interior finishing of flooring areas, which are subdivided into smaller zones to represent distinct workspaces. Task durations are estimated using a beta distribution, with potential spatial conflicts considered. Several alternative strategies for organizing the workflow are then tested to identify the most effective approaches for optimizing spatial planning. Findings The findings show that three spatial management strategies significantly improved performance. First, defining clear work zones reduced spatial conflicts by 68.3% and cut delays from 12.97% over the ideal two-team time in the unplanned case to 3.47%. Second, implementing structured work patterns, such as the serpentine approach, further limited team interference and provided a more predictable workflow. Third, combining work zones with optimized starting positions achieved near-ideal performance, with only 1.01% delay over the ideal and 94.7% fewer conflicts than the unplanned scenario, demonstrating the strong impact of strategic spatial planning on reducing delays and enhancing resource use. Originality/value This paper contributes to the research field by highlighting the critical role of strategic spatial planning as well as providing guidance for practitioners with directly applicable on-site spatial management strategies for avoiding production congestion when increasing manning.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".