MILP model for simultaneous batching, production and distribution operations in single-stage multiproduct batch plants
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
Traditionally, the short-term production and distribution activities have been addressed with a decoupled and sequential methodology. Although this approach simplifies the problem, there are several environments where it generates inefficiencies or is simply not applicable. Consequently, the integration of both problems is very valuable in a variety of industrial applications, especially in industries where final products must be delivered to customers shortly after production. This paper presents a mixed-integer linear optimization model that simultaneously solves the production and distribution scheduling in a single-stage multi-product batch facility with multiple non-identical units operating in parallel, where transportation operations are carried out with a heterogeneous fleet of vehicles. As operations are performed in a batch environment, the production and distribution problems also integrate decisions related to the number and size of batches required to meet the demand for multiple products. The capabilities of the proposed approach are illustrated through several cases of study. Finally, these examples are solved with a two-stage approach and the superiority of the solutions using the integrated approach is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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