Conveyor Throughput Optimization at a Distribution Centre
Bibliographic record
Abstract
The conveyor system is one of the most popular material handling systems in production and warehouse facilities due to high throughput and safety. The throughput rate of such systems is an essential performance measure. The congestion of the conveyor is a significant problem and as such this issue requires serious attention. FedEx Supply Chain, the 3PL provider for the Canadian Tire distribution centre located at Coteau-du-Lac, Quebec faces productivity issues for their outbound operations during high volume periods. The distribution centre staff at the Company has developed, over the years, their own procedures to prevent bottlenecks at the conveyor. Nevertheless, it remains a challenge to implement different operational scenarios to optimize the throughput. Furthermore, there are a number of operational variabilities along the conveyor, whereas the outbound operations follow a schedule of picking cycles. Hence, a comprehensive simulation model is required to capture various variabilities, identify possible bottlenecks, and predict the effects on throughput of applying different levers. The findings obtained based on the experimentations are analyzed and managerial recommendations are provided. Finally, areas for future research are highlighted.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".