The Vehicle Routing Problem with Floating Targets: Formulation and Solution Approaches
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses a generalization of the vehicle routing problem in which the pick-up locations of the targets are nonstationary. We refer to this problem as the vehicle routing problem with floating targets and the main characteristic is that targets are allowed to move from their initial home locations while waiting for a vehicle. This problem models new applications in drone routing, ridesharing, and logistics where a vehicle agrees to meet another vehicle or a customer at a location that is away from the designated home location. We propose a Mixed Integer Second Order Cone Program (MISOCP) formulation for the problem, along with valid inequalities for strengthening the continuous relaxation. We further exploit the problem structure using a Lagrangian decomposition and propose an exact branch-and-price algorithm. Computational results on instances with varying characteristics are presented and the results are compared to the solution of the full problem using CPLEX. The proposed valid inequalities reduce the computational time of CPLEX by up to 30% on average while the proposed branch and price is capable of solving instances where CPLEX fails in finding the optimal solution within the imposed time limit.
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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 | 0.002 | 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 | 0.001 | 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 it