A scenario-based approach for a green aggregate production planning in a multi-site manufacturing system with workforce transferring
Bibliographic record
Abstract
This paper presents an Aggregate Production Planning (APP) model that minimizes workforce-related costs in multi-site manufacturing systems by introducing an inter-site workforce transferring policy. This novel approach reallocates workers between sites, reducing the need for hiring or layoffs. Additionally, the APP integrates environmental objectives, directly minimizing energy consumption, carbon emissions, and waste generation, forming a Green APP (GAPP). To address uncertainties in subcontractors’ costs, capacity, and overtime production, a scenario-based approach is adopted. The problem is formulated as a multi-objective scenario-based mixed-integer linear programming model and solved using a combination of the LP-metric method and fuzzy AHP. The model was validated through test problems and real-world implementation, achieving over 60% cost reduction in workforce changes. This study contributes to the APP literature by proposing inter-site workforce transfer policy, integrating environmental considerations, and addressing uncertainties in subcontractor performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".