Aggregate production planning through memetic passing vehiclesearch
Bibliographic record
Abstract
Abstract: Aggregate production planning (APP) deals with the simultaneous determination of plant’s production, inventory and vocation levels over a finite time horizon. The aim of aggregate production planning is to finalize overall output levels in the near to medium future in uncertain demands. In this paper, three different cases of aggregate production planning problems are presented and optimized by using Passing Vehicle Search (PVS) algorithm. To overcome the poor convergence of PVS algorithm, its performance is experimented by combining it with conjugate gradient (CG) optimization method. PVS algorithm is efficient is finding global optimum solutions whereas CG method is an effective method to search local optimum solutions. Proposed memetic PVS combines the global search capabilities of basic PVS and efficient local search of CG method to address challenging problems of aggregate production planning. The experimental results demonstrates that combined PVS-CG is more efficient compared to basic PVS algorithm and CG method in solving APP problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".