A Hierarchical Intermodal Network Optimization Model for the Gasification of Recycled Plastic in Southern Ontario: The Case of McKeil Marine
Bibliographic record
Abstract
In this research, we apply the hierarchical fixed charge uncapacitated facility location model to optimize the supply chain network of a gasification plant. The model determines the optimal location of shredding facilities on the network that are required to supply plastic feedstock to the gasification plant in Hamilton, Ontario. To construct the model, we will determine an optimized transportation network for the gasification plant, which faces a fixed demand during the period of one-year. The transportation routes to the plant and the associated costs are determined with the use of geographic information systems software. Our research finds (1) the optimal number of shredding facilities and their locations (2) the production of each facility and allocation of demand in a one-year period and (3) an optimized transportation network that attempts to utilize intermodal transportation in order to minimize the cost function of the entire network. The model determines that transporting the plastic via truck and locating a single shredding facility in Hamilton is more cost effective than decoupling the shredding process from the plant or transporting the plastic via an alternative modality. This leads the author to the conclusion that the network’s scale is too small and that there is not a large enough volume of plastic flowing through the network to justify the utilization of an intermodal transportation network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".