Makespan optimization in recycling-integrated flow shop scheduling using a modified NEH heuristic with industrial case study
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
Recycling in manufacturing is becoming increasingly crucial as industries seek to reduce environmental impact and improve operational efficiency. Introducing recycling at the initial stages of the production process plays a critical role in minimizing material waste, conserving natural resources, and promoting sustainable manufacturing. Considering these advantages, integrating recycling into core manufacturing workflows becomes a strategic priority. This study addresses the Flow Shop Scheduling Problem (FSSP), a classical optimization problem in operations research, by integrating a recycling mechanism into the FSSP framework. The problem considers n jobs and m machines, aiming to determine an optimal job sequence that minimizes the makespan while considering recycling activities. An enhanced NEH heuristic is developed to solve this modified FSSP, and its performance is validated using standard benchmark instances. The results demonstrate that incorporating recycling significantly improves production efficiency and offers meaningful insights for advancing sustainable manufacturing practices. A practical industrial case is also examined to illustrate the real-world relevance of the proposed model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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