A multi-phase integrated scheduling method for cloud remanufacturing systems
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
• A framework for cloud remanufacturing, encompassing a series of remanufacturing macroscopic phases, is established. • A multi-phase integrated scheduling problem for the proposed cloud remanufacturing system is introduced. • A mathematical model is developed to explain the scheduling problem. • An improved whale optimization algorithm integrating enhanced population updating mechanisms is designed to address this problem. The cloud remanufacturing system embraces a series of interdependent remanufacturing macroscopic phases (RMAs) with intricate precedence relationships, increasing the complexity of task scheduling and resource allocation. Thus, the multi-phase integrated scheduling is necessary to manage remanufacturing tasks and optimize resources and capabilities effectively in the cloud environment. This research investigates the multi-phase integrated scheduling problem for cloud remanufacturing system involving a series of RMAs including initial inspection, disassembly, reprocessing, reassembly, and final test. A mathematical model is created to explain the scheduling issue using the suggested cloud remanufacturing framework. Due to the high complexity of integrated scheduling, traditional meta -heuristic algorithms cannot be directly applied to solving the problem. Thus, an improved whale optimization algorithm (IWOA) incorporating the self-adaptive weighting and quadratic interpolation techniques is proposed for addressing the studied problem efficiently. A case study is designed and conducted, and the findings indicate that the IWOA is more effective than other methods in addressing the proposed complex scheduling issues with better accuracy, faster computation, and improved convergence efficiency.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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