Collaborative Allocation Optimization of Production Line Workers Based on Multidimensional Feature Measurement and E-CARGO Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is challenging to achieve an optimal worker allocation for production lines of large-scale industrial enterprises due to the complex requirements for workers’ capabilities. Some optimization approaches have been proposed to solve this problem from different perspectives. However, they have not fully explored the multidimensional features of both workers and production lines, making it hard to obtain an optimal allocation. This article proposes a novel approach to collaborative allocation optimization of production line workers by incorporating multidimensional feature measurement and the environment-classes, agents, roles, and objects (E-CARGO) model. First, we develop a comprehensive evaluation system to quantify the diverse features of workers and production lines. Based on it, an adaptability assessment mechanism is designed to measure the matching degree of workers for different production lines. Afterward, the role-based collaboration theory and the E-CARGO model are innovatively utilized to formalize the worker allocation problem. Meanwhile, the key constraints are identified to guarantee the reasonability of allocation, and an efficient solution via CPLEX package is proposed. Finally, the case analysis and simulation experiments verify the effectiveness of the proposed approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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