An Optimization Model for Smart and Sustainable Distributed Permutation Flow Shop Scheduling
Bibliographic record
Abstract
Smart production scheduling has gained significant attention due to advancements in industrial informatics and technologies that enable the monitoring, control, and adaptation of task scheduling in response to disruptive events. These events can include machine breakdowns, variations in task processing times, and the arrival of new or unexpected tasks. Concurrently, sustainable production scheduling aims to optimize task scheduling by considering economic, environmental, and social factors. This paper introduces a novel optimization model for the development of smart and sustainable production scheduling in a distributed permutation flow shop. The proposed model aims to minimize the makespan while simultaneously limiting the number of lost working days and energy consumption. It also strives to increase job opportunities within acceptable limits. To evaluate the proposed model, we conduct numerical simulations using various examples and a real-case study focusing on auto workpiece production. The results demonstrate the superior performance of the proposed model. Sensitivity analyses are performed to assess the model's ability to deal with disruptions and uncertainties while satisfying economic, environmental, and social considerations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".