A robust service composition for a resilient cloud manufacturing service network
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
Modern manufacturing systems are undergoing a profound transformation through digitalization and interconnected processes, culminating in advanced paradigms like cloud manufacturing. However, ensuring the resilience of these systems against disruptions remains a critical challenge. This research addresses this gap by introducing a robust service composition strategy to enhance the resilience of cloud manufacturing networks. A Mixed-Integer Nonlinear Programming (MINLP) model is developed, incorporating subentropy to handle uncertainties across diverse scenarios. To solve the model, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) are employed, with PSO demonstrating superior performance. The proposed framework is validated using a real-world case study of ventilator production during the COVID-19 pandemic, showcasing its ability to enhance resilience through efficient resource allocation and industry collaborations. For Solving, PSO, GA, and SA algorithms are employed which PSO demonstrated superior performance. Comparative results highlight robustness of the model and efficacy of PSO in optimizing service compositions. This study makes a novel contribution by introducing subentropy-based uncertainty management to the field of cloud manufacturing and provides practical insights for designing resilient manufacturing networks. These findings have significant implications for both academics and practitioners, offering a comprehensive framework to improve the adaptability and continuity of cloud-based manufacturing systems.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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