A particle swarm optimisation for fuzzy dynamic facility layout problem
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Bibliographic record
Abstract
Dynamic facility layout problem (DFLP) deals with arranging and rearranging the layout plan of a manufacturing system or a service provider throughout several periods. In each period, the material handling costs are different from the previous periods due to the change in the market demand and product mix. Since the problem is NP-hard, numerous approaches have been proposed in the literature in order to find near-optimum solutions of the DFLP. In this paper, we model the natural uncertainty in material handling costs with fuzzy theory. A fuzzy particle swarm optimisation (FPSO) algorithm is proposed to solve the problem. We implement a number of ranking criteria from the literature in order to test the performance of the developed algorithm. Computational results confirm the efficiency and effectiveness of the proposed mechanisms.
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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 | 0.000 | 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 it