Recall cost-time tradeoffs for remanufacturing shop lot streaming scheduling problem with non mixed production using an improved non-dominated sorting genetic algorithm
Bibliographic record
Abstract
In this paper, we study the problem of lot streaming scheduling in a remanufacturing shop with consistent sublots, where mixed production is not allowed between sublots possessing different types of remanufacturable parts. The problem is formulated as a multi-objective optimization problem with optimization objectives of recall cost and completion time. Such problems are NP-hard and need to be solved using an improved non-dominated sorting genetic algorithm. Two vectors regarding sublot size allocation and sublot processing order determination together form a solution. In order to improve the quality of the solution, the algorithm uses a randomization strategy and two heuristics to initialize the population and introduces dynamic genetic operations to advance the population diversity. On the one hand, the designed four types of genetic operators are dynamically selected according to the number of iterations. On the other hand, the elite retention strategy is improved, i.e., based on the probability that one of the individuals performing the crossover operation can come from the memory bank. Both numerical experiments and real case solving verify the effectiveness of the developed algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".