Minimizing personnel assignment costs in the layout phase of steel component construction
Bibliographic record
Abstract
Purpose To enhance the efficiency of personnel assignment in layout operations, this study employs 0–1 integer programming optimization theory, integrated with practical layout operations in steel structure construction, to assign manpower under known constraints to find the optimal personnel assignment and minimal cost and to provide management decision-makers with an effective way to control operational costs. Design/methodology/approach The research considers constraints such as personnel availability and part types, constructing a mathematical model with one objective function and 11 constraints. By applying this model to a real-world project for constructing a technology factory and using LINGO 18.0 software, the study demonstrates that optimizing personnel assignment can reduce total costs by 12.82% and manual assignment time by 99%. Findings The findings also elucidate the following: (1) Sensitivity analysis indicates that decreasing the number of working days slightly increases total operational costs, whereas extending the duration to 30–40 days results in cost reductions. Furthermore, reducing high-efficiency and high-salary personnel has a minor impact on overall costs, while cutting low-efficiency and low-salary personnel leads to a substantial cost increase of approximately 50–52%. (2) Efficient and rapid personnel assignment contributes to achieving minimal operational costs, with a cost reduction of 12.82%, while also facilitating the attainment of project goals in the most effective manner. Originality/value The study developed a mathematical model featuring a single objective function and 11 practical constraints, providing practitioners with the flexibility to adapt it as needed to address empirical engineering challenges related to personnel layout assignments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".