Implementing Off-peak Deliveries in the Greater Toronto Area: Costs, Benefits, Challenges
Bibliographic record
Abstract
Abstract Nestle Canada currently uses 32 routes that serve over 4,500 customers in the Greater Toronto Area (GTA). This study aims to quantify Nestlé's costs and benefits of modifying their ice cream supply chain to incorporate night-time deliveries, while providing a framework for the regulatory, conceptual, and inertial obstacles to implementation. Employing Nestlé's customer data set, we created routing software to determine the proportion of customers who must be willing to accept deliveries outside of normal working hours so that the change would be financially feasible. Based upon a literature review we found that, before proceeding, the following qualitative factors should be considered: safety, sustainability, regulatory concerns, truck noise, traffic, and congestion. Reduction of 3–10 percent in the number of routes may result from switching a suitable proportion of deliveries to night-time, achieving the minimum fleet size when 50–60 percent of locations are served on night routes. The operation of both night-time and daytime deliveries would enable an increase in truck utilization, thus decreasing the number of vehicles required. Recommendations for success of night-time deliveries include preparation of a safety plan, procurement of plate trucks, noise-abatement techniques, and the development of a noise-monitoring program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".