Multi-Objective Optimization and Cyber-Physical Integration for Sustainable Manufacturing in Industry 5.0
Bibliographic record
Abstract
Furthermore, the development of manufacturing from Industry 4.0 to Industry 5.0, requires paradigm changes towards sustainable, resilient and human-centered industrial ecosystems. In this paper, a methodology is proposed with the aim of designing a holistic framework for the integration between MOO and CPS to render sustainable manufacturing in a way that is congruent with Industry 5.0 principles. Using a system-theories point of view and data-driven intelligence, we will cope with trade-offs of contradicting objectives (productivity, energy efficiency, environmental impact, or well-being of workers) in the dynamic factory context. Our approach is based on real-time sensor data, digital twins and machine learning for decision support throughout the product lifecycle. We introduce an optimization framework which integrates Pareto-based evolutionary algorithms and reinforcement learning for adaptive control in complex CPS. In addition, we introduce human-in-the-loop-design to guarantee transparency, to empower workers, and to improve system resilience in the face of uncertainty. Taking case studies in smart manufacturing, our framework realizes remarkable energy efficient (up to 27%), carbon footprint decreased (by 18%), and worker satisfaction score improvement, verifying its practicality in human-cyber-physical coordination. We also discuss policy implications and ethical issues related to responsible technology implementation. This work adds to growing debate on Industry 5.0 by bridging computational techniques with social technical viewpoints. It offers a flexible, integrated model for future-fit manufacturing systems which are based on commitment to sustainability, resilience and human well-being. The approach provides theoretical understanding as well as practical instruments for engineers, researchers and policy makers to create a balanced, post-industrial manufacturing system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".