Scheduling Customized Orders by Considering the Ergonomic Constraints: A Case Study at YEMTAR Company
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Bibliographic record
Abstract
It is important for companies to meet customer demands by due date and reduce the labor cost on the finalized product. For this purpose, order scheduling is required for different purposes such as minimizing makespan, maximizing resource utilization, etc. Dynamic production environment causes stochastic operation times at companies which work based on project type labor-intensive production. Stochastic operation times make order scheduling harder. There are many reasons that causes operation times being stochastic such as technical specifications of the orders, skills of the operators, bottlenecks in the job-shop, and etc. However, one of the most important but less discussed constraints that affect the probability distribution of the operation times is the ergonomic constraint. Ergonomic constraints, such as musculoskeletal discomfort, fatigue and limitations determined by the laws make it even more difficult to predict the total makespan of waiting orders. In this study, an order scheduling algorithm that considers the dynamical production environment and the ergonomic limitations is proposed for nearly optimizing average makespan for several waiting orders in the grinding and painting workstation of YEMTAR Company. The proposed algorithm adopts the technical order specifications and ergonomic constraints together, computes the stochastic operation times by using simulation, and schedule orders by using genetic algorithm. The objective is to determine the entry sequence of the waiting orders to the workshop for minimizing their average makespan which directly influences the resource utilization, efficiency, and labor costs.
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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 | 0.000 | 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 | 0.000 |
| 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