Machine learning techniques and multi-objective programming to select the best suppliers and determine the orders
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
Selection of appropriate suppliers and allocation the orders among them have become the two key strategic decisions regarding purchasing. In this study, a two-phase integrated approach is proposed for solving supplier selection and order allocation problems. Phase 1 contains four techniques from statistics and Machine Learning (ML), including Auto-Regressive Integrated Moving Average, Random Forest, Gradient Boosting Regression, and Long Short-term Memory for forecasting the demands, using large amounts of real historical data. In Phase 2, suppliers’ qualitative weights are determined by a fuzzy logic model. Then, a new multi-objective programming model is designed, considering multiple periods and products. In this phase, the results of Phase 1 and the results of the fuzzy model are utilized as inputs for the multi-objective model. The weighted-sum method is applied for solving the multi-objective model. The results show Random Forest model leads to more accurate predictions than the other examined models in this study. In addition, based on the results, the selection of the forecasting techniques and different weights of suppliers affect both supplier selection and the related orders.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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