Enhancing socioeconomic sustainability in glass wall panel manufacturing: An integrated production planning approach
Bibliographic record
Abstract
• Novel exploration of production planning complexities in labor-intensive industries. • Integration of social sustainability principles into scheduling models. • Development of a unique scheduling framework prioritizing worker well-being. • Validation of model efficacy through real-world production data analysis. • Comparative analysis reveals significant advantages of integrated socio-economic scheduling approach. While conventional production planning approaches prioritize short-term efficiency and economic gains, the sustainability development objectives emphasize a holistic perspective, integrating eco-friendly practices, social responsibility, and economic viability. Nevertheless, the existing literature overlooks a gap in understanding the role of socio-economic factors in labor-intensive production processes. In this regard, this research aims at investigating the impact of social factors, such as labor skill level and experience, on production planning, with a specific focus on glass wall panel manufacturing. The research integrates sustainability socioeconomics, as embodied by an empirically developed labor learning curve, with the MINLP (Mixed-Integer Nonlinear Programming) scheduling model. The results show that the integrated socio-economic scheduling approach outperforms traditional scheduling approach, reducing idle time up to 43% and promoting more balanced production distribution. Despite slightly higher upfront production costs, the integrated model offers long-term cost savings through reduced idle time and overtime, making it a viable option for companies seeking to improve productivity and worker satisfaction. The implementation of this work is recommended to maintain a sustainable, safe, and healthy work environment while also considering long-term economic benefits rather than short-term profits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".