Truck and Unmanned Vehicle Routing Problem with Time Windows: A Satellite Synchronization Perspective
Bibliographic record
Abstract
We consider an important feature of satellite synchronization in the practical scenario of using unmanned vehicles (UVs) carried by trucks for “last-meter” delivery and introduce the truck and UV routing problem with time windows (TUVRP-TW) for optimizing the routes of a homogeneous fleet of truck-UV combinations. A UV that has been dispatched from its truck must be picked up by the same truck or must return by itself to the depot. Customers with time windows are classified into two types: truck-UV customers (TUCs) and UV customers (UCs). The TUCs where trucks dispatch or pick up the carried UVs are regarded as satellites. Fleet coordination and satellite synchronization are essential for modelling the TUVRP-TW. We classify satellite synchronization into inner-satellite synchronization and intersatellite synchronization. The inner-satellite synchronization generally considered in the literature focuses on synchronization operations at the same satellite. Intersatellite synchronization, which focuses on synchronization operations at various satellites, allows UVs to not return to the dispatched locations, if necessary. In the mixed-integer linear programming model of the TUVRP-TW, both binary variables for identifying the appointed satellites and continuous variables for time continuity constraints are introduced to ensure the interaction between truck routes and UV routes. A hybrid algorithm based on a greedy randomized adaptive search procedure (GRASP) and a variable neighborhood search (VNS) is provided. Based on generated instances and benchmark instances, computational experiments are conducted to evaluate the performance of the intersatellite synchronization, the performance of the developed formulation, and the applicability of the hybrid algorithm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".