Scheduling Customized Orders: A Case Study at BEST Transformers Company
Bibliographic record
Abstract
Stochastic operation times make job-shop scheduling harder at companies which work based on project type laborintensive production in a dynamic environment.The operation times are not known before production and change based on the orders' technical specifications.In performing required operations with the aim of producing a final product, scheduling is required for different purposes such as minimizing makespan, maximizing resource utilization, etc.This is important as it enables companies to meet customer demands by due date and reduce the labor cost on the finalized product.In this study, an order scheduling algorithm is proposed for nearly optimizing average makespan for several waiting orders in a transformer company's core production workshop considering dynamical production environment.The proposed algorithm adopts the technical order specifications, computes the stochastic operation times making use of simulation, and schedule orders using one of the widely used meta-heuristics, namely genetic algorithm.The objective is to determine the entry sequence of the waiting orders to the core production workshop for minimizing their average makespan which directly influences the resource utilization, efficiency, and labor costs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".