Green pickup and delivery problem with private drivers for crowd-shipping distribution considering traffic congestion
Bibliographic record
Abstract
Crowd-shipping, employing private drivers to partially replace company-owned trucks in distribution, has emerged as a prominent trend for its cost-effectiveness and sustainability. While crowd-shipping is known as a distribution pattern that combines economic efficiency and environmental benefits, however, the frequent occurrence of traffic congestion has made this pattern less effective than it should be. In this research, the problem of vehicle routing optimization under traffic congestion is investigated from the perspective of simultaneously reducing environmental pollution and costs. Considering private drivers picking up and delivering parcels on the way, this study incorporates the objective of minimizing transport as well as particulate matter (PM) and nitrogen oxides (NOx) emission costs into route optimization for crowd-shipping and proposes a Green Pickup and Delivery Problem with Private Drivers (GPDP-PD). To be more realistic, vehicle speeds depend on the level of traffic congestion, reflecting the time-dependent nature of the proposed model. An improved adaptive large neighborhood search (ALNS) algorithm is developed, and computational experiments are conducted to demonstrate the efficiency of the improved ALNS. Case studies show that there is uncertainty about the environmental benefits of crowd-shipping under traffic congestion. Our proposed model is capable of efficiently allocating private drivers and optimizing vehicle routes according to road conditions, thus identifying the crowd-shipping operational scheme with the lowest cost and emissions. Moreover, a time limit of 0.7-0.8 h and the low cost of private drivers can achieve environmental and economic benefits simultaneously. It provides useful insights into the sustainability of logistics and distribution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".