Level schedules for mixed-model JIT production lines: characteristics of the largest instances that can be solved optimally
Why this work is in the frame
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Bibliographic record
Abstract
Takt time and cycle time are design variables in JIT production. Actual production is a performance variable. The level production scheduling problem constructs a schedule that matches actual production to takt time and cycle time. The problem can be solved optimally by constructing a network of nodes and arcs in which each path through the network corresponds to a production schedule and the shortest path corresponds to the optimal level production schedule. When a problem instance is large the number of nodes and arcs is very large. It is critically important to (i) eliminate from the network nodes and arcs that cannot be on the shortest path and (ii) evaluate the remaining nodes and arcs efficiently. This paper examines the best algorithm for finding an optimal schedule and analyses, by solving previously unsolved instances from the literature, characteristics of the largest instances that can be solved optimally.
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 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