Strategic Planning of an Inter-connected Crowd Logistics Network
Bibliographic record
Abstract
There is a growing market for crowd-shipping, which hires people to transport packages on their regular commutes, in the Greater Toronto Area (GTA). This thesis considers a hybrid crowd-shipping operation that hires crowd-shippers and regular drivers in a physical internet environment. The spatial distribution of potential crowd-shippers is analyzed by leveraging a behaviour model and data that combines 2016 Transportation Tomorrow Survey (TTS) data and census data. An inter-connected crowd logistics network is designed to serve business-to-consumer (B2C) logistics demand through solving a parcel locker location routing problem (PLLRP) that optimizes the placement of parcel lockers and the movements of parcels. Finally, the service levels of the proposed network are estimated. The numerical results reveal that an inter-connected crowd logistics network can provide more stable and cost-effective services when the demand level is higher. The hiring of regular drivers is necessary to service some regions with low crowd-shipper supply.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".