A discussion of production planning approaches in the process industry
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Bibliographic record
Abstract
In this paper, we discuss the literature on production planning approaches in the process industry. Our contribution is to underline the differences, as well as the similarities, between issues and models arising in process environments and better known situations arising in discrete manufacturing, and to explain how these features affect the optimization models used in production planning. We present an overview of the distinctive features of process industries, as they relate to production\nplanning issues. We discuss some of the difficulties encountered with the implementation of classical flow control techniques, like MRP or JIT, and we describe how various authors suggest to solve these difficulties. In particular we focus on the concept of "recipe", which extends the classical Bill of Materials used in discrete manufacturing, and we describe how the specific features of recipes are taken into account by different production planning models. Finally,we give a survey of specific flow control models and algorithmic techniques that have been specifically developed for process industries.
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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 | 0.001 |
| 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