A simulation study of capacity utilization in a third-party logistics provider warehouse
Bibliographic record
Abstract
ABSTRACT \n \nA simulation study of capacity utilization in a third-party logistics provider warehouse \n \nChristiaan Casilimas Jacome \n \nNowadays companies focus more on their core business operations and subcontract third parties to perform services that were traditionally performed in-house. A third-party logistics provider (3PL) is an organization used to outsource elements of the supply chain like transportation and warehousing services. In 2019 this industry contributed about 90 billion CAD to the total Canadian GDP. \nOne of the biggest constraints in warehousing, is the effective management of warehouse capacity. Warehouses are physically limited by their layout, number of pallet positions, number of dock doors, size of the storage locations, size of the aisles, among others. Additional challenges may arise due to the seasonality of storage requirements. 3PL providers offering warehousing services are thus interested in determining at which levels of used capacity a warehouse is more profitable. This information can then be used to develop an order-accepting strategy for the company to be used during low and high seasons. \nThe main contribution of this thesis is to develop a simulation tool that provides a good starting point to answer these questions. In particular, the thesis focuses on a case study involving the operations of one particular 3PL warehouse with a major customer who moves only full pallets. This 3PL handles temperature sensitive products using a turret truck in a narrow aisle work environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".