The flexible park-and-loop routing problem
Bibliographic record
Abstract
This work investigates a variant of the vehicle routing problem inspired from last-mile delivery operations, incorporating explicit parking considerations and various types of flexibility. The first type of flexibility stems from the availability of parking spots, i.e., each driver may select from one of the available time windows to park at a parking spot. Time-based and space-based flexibility from customers are simultaneously exploited to reduce overall operational costs. To solve the problem, we propose a Mixed-Integer Linear Programming (MILP) model and devise a Hybrid Large Neighborhood Search (HLNS) algorithm, which embeds problem-specific destroy and repair operators, as well as a tailored dynamic programming to improve the configuration of a route. Within the HLNS, a set partitioning model is solved periodically as an attempt to find a combination of routes of better quality by utilizing the pool of routes collected so far. The effectiveness of HLNS is demonstrated on a set of newly generated instances and two special cases existing in the literature. Additionally, we demonstrate the impact of parking-related flexibility toward the solutions and show the difference in impacts resulting from considering each customer-related flexibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".