Layout Optimisation for Command Spaces with Unequal-sized Workstations Using a Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
A genetic algorithm has been developed to analyze layout options of military command spaces. It adopts a fitness function that reflects a human factors characterization of interoperator collaboration efficiency. A novel solution was proposed to examine command space layouts that involved unequal-sized workstations. The solution takes advantage of the unique parameter setup of the fitness function and converts an unequalsized facility layout problem into an equal-sized one by introducing the concept of a standard unit workspace. The effectiveness of this solution was investigated in a simulation experiment where the algorithm was used to analyze design options of a hypothetical operations room layout that involved a 9-person team with unequal workspace requirements. Based on 100 simulation runs, 24 unique layout options were identified by the algorithm and their optimality was confirmed by analytical examination. The proposed solution allows human factors practitioners to address the layout configuration of command spaces with complex workspace requirements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it