Capacitated Multi-objective Disassembly Scheduling with Fuzzy Processing Time viaAFruit Fly Optimization Algorithm
Bibliographic record
Abstract
Abstract This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete products remanufacturing process. It is meaningful to study a novel model and algorithm based on uncertainty processing time to solve uncertainty disassembly scheduling problems. Therefore, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of products disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability performance. Finally, a case study is described to illustrate the proposed method. The main finding of this current work is to provide a new idea to solve the problem of disassembly scheduling in an uncertain environmental practically and efficiently.
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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 | 0.001 |
| 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) | 0.003 | 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".