Research on delivery optimization of food delivery orders based on crowdsourcing platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continuous expansion of the food delivery market, the challenges of order allocation and route optimization in crowdsourced delivery have emerged as a critical research focus. While existing studies primarily concentrate on meeting customer demands, improving delivery efficiency, and reducing costs, they often overlook the interests of delivery riders. Addressing this gap, this paper establishes maximum working hours as a constraint for riders while ensuring service quality and maximizing their earnings. A comprehensive weighting system is designed to balance three key factors: delivery time, order deadlines, and rider compensation. The proposed algorithm optimizes routes through a greedy insertion method, aiming to deliver high-quality solutions within riders' available timeframes. Through computational simulations, the study provides actionable recommendations for enhancing order allocation and route optimization in crowdsourced delivery systems.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it