Supplier selection and order lot sizing using dynamic programming
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
In this paper, we consider a multi-period integrated supplier selection and order lot sizing problem where a single buyer plans to purchase a single product in multiple periods from several qualified suppliers who are able to provide the required product with the needed quality in a timely manner. Product price and order cost differs among different suppliers. Buyer's demand for the product is deterministic and varies for different time periods. The problem is to determine how much product from which supplier must be ordered in each period such that buyer's demand is satisfied without violating some side constraints. We have developed a mathematical programming model to deal with this problem, and proposed a forward dynamic programming approach to obtain optimal solutions in reasonable amount of time even for large scale problems. Finally, a numerical example is conducted in which solutions obtained from the proposed dynamic programming algorithm is compared with solutions from the branch-and-bound algorithm. Through the numerical example we have shown the efficiency of our algorithm.
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 | 0.000 |
| 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