A robust service composition for a resilient cloud manufacturing service network
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".