Integrated scheduling of distributed manufacturing with assembly and distribution: state of the art, challenges, and future directions
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
Distributed manufacturing systems are very complex scheduling problems due to strong interactions between the production, distribution, and assmebly stages. This review paper surveys recent studies on modelling and optimising distributed manufacturing systems based on integrated scheduling problems. A total of 81 articles published in SCI- and SSCI-indexed journals from January 2020 to August 2025 are reviewed. First, we classify the integrated scheduling problems according to optimisation objectives, number of objectives, constraints, and the presence of uncertainties. Second, we analyse the use of meta-heuristics, highlighting algorithm structures, single- and multi-objective formulations, performance evaluation metrics, stopping criteria, and benchmark instances. Third, we identify key challenges and limitations in applying meta-heuristics to these complex scheduling problems. Finally, we summarise the current state of the field and propose promising directions for future research.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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