Minimizing the sum of earliness and tardiness in the multi-factory two-stage assembly scheduling problem
Bibliographic record
Abstract
The geographical dispersion of collaborative factories can provide cost-saving potential for manufacturers and lead them to easier fit the global markets. On the other hand, in such networks of factories, coordination is especially important for the just-in-time delivery of orders. This study investigates a new configuration of factories in a network of collaborative manufacturing. In the first stage, some independent suppliers produce and deliver the different components of the final products to the assembly factory. Each supplier can produce a particular component of a final product. In the assembly factory, the final products are assembled. The objective is to determine the optimal schedule of the jobs in each factory to minimize the sum of earliness and tardiness. A mixed-integer formulation for this problem is proposed, which can find the optimal solution for the small-size instances. The iterated local search methods are also developed to cope with larger instances. Computational experiments show that the iterated local search methods outperform the well-known iterated greedy method in literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".