Incorporating learning and fatigue effects in flowshop scheduling: a case study
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
Modern manufacturing systems promote a human-centred approach that emphasises the integral role of humans in the manufacturing process. Human operations, however, can be influenced by phenomena such as learning and fatigue. Within the framework of the flowshop scheduling problem, a bi-objective approach is developed to simultaneously minimise both makespan and total fatigue dose. This modular strategy follows a human-centric design by integrating the learning process as a variable in scheduling problems and incorporating the operator's fatigue. An experimental protocol is proposed to collect and estimate the learning rates of picking workers, as well as to mathematically model the learning effect. In addition, a methodology for collecting and analyzing data on muscular fatigue is outlined, allowing estimation of fatigue rate parameters. The findings of this study contribute to multiple fields. The study introduces an innovative method to define and incorporate the effects of learning and fatigue in the problem studied. It also affords managerial insights into the effective management of break policies, particularly in picking line operations, which have been poorly explored in the literature. Future research perspectives to extend this approach to other contexts are also suggested.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it