Enhancing Supplier Selection and Order Allocation Processes with Machine Learning Clustering and Optimization Techniques
Bibliographic record
Abstract
Optimizing supply chain management relics on effective Supplier Selection and Order Allocation (SS&OA). This study introduces a novel two-phase framework that exemplifies a sophisticated approach to SS&OA, demonstrating the efficacy of integrating modern data-driven methodologies to enhance decision accuracy. In the initial phase, K-means Gaussian Mixture Model, and Balance Iterative Reducing and Clustering techniques are utilized to identify and group suitable suppliers based on managerial preferences and specific requirements. The accuracy of the clustering models is assessed through the Silhouette Score technique. The second phase introduces a state-of-the-art multi-objective optimization model for SS&OA, strategically employing suppliers shortlisted from the most effective Machine Learning (ML) clustering method in Phase 1. The mathematical model has been developed encompassing considerations for multi-source, multi-period, and multi-product scenarios. Validated with real historical contract data from Canada, this framework highlights ML clustering techniques' substantial impacts on decision accuracy, offering valuable insights for data-driven SS&OA decisions.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".