A new approach for the cellular manufacturing problem in fuzzy dynamic conditions by a genetic algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a fuzzy linear mix-integer programming model for design of cellular manufacturing systems with fuzzy part demands and product mix changeable under a multi-period planning horizon. In this dynamic condition, the best cell design for one period may not be efficient for subsequent periods and the reconfiguration of cells is required. The proposed model can determine the production volume for each part considering its fuzzy demand. The other advantages of the proposed model are as follows: considering inter-cell material handling with constant batch size, alternative process plan for part types, operation sequence, machine relocation, machine replication, machine utilization and cell number flexibility. Main constraints are the cell size, machine capacity and production volume limitations. The objective is to minimize the sum of the constant/variable/relocation machine costs as well as inter-cell movements cost. Because of the complexity of the proposed model, which is a combinatorial nonlinear optimization, we develop an efficient genetic algorithm with novel representation and operators for solving the proposed model. 29 small, medium and large-sized problems are generated to evaluate the performance of the proposed model and the efficiency of the developed genetic algorithm.
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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 | 0.001 | 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