Um modelo para o problema de dimensionamento e sequenciamento de lotes com aceitação de pedidos
Bibliographic record
Abstract
This paper presents a mixed integer programming model with the age of the product in inventory for a lot-sizing and scheduling problem with order acceptance.In this problem, customer demand is aggregated into orders, that can be accepted or not.Orders must be delivered within a time window.Each item can keep in stock for a determinate time (shelf-life).The aim of the problem is to maximize the profit obtained by orders acceptance, discounting the costs of inventory and setup costs.Two constructive heuristics are developed, and the branch-and-cut of the CPLEX resolver is also used to obtain a solution to the problem.Computational tests were performed and https://proceedings.science/p/106930?lang=pt-br the results obtained were analyzed.The heuristics performed, on average, better than the branchand-cut of the CPLEX in obtaining good quality solutions within the established time limit.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".