A hybrid genetic algorithm for the vehicle relocation problem with ride-sharing options in one-way car-sharing systems
Why this work is in the frame
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Bibliographic record
Abstract
The imbalance of idle cars at different stations remains a critical challenge in one-way car-sharing systems. This paper proposes a novel mixed user-operator-based relocation strategy for this problem. In this one-way car-sharing system, ride-sharing service is allowed, and customers can share trips with others by a rental vehicle. Ride-sharing, as a supplement to operator-based relocation, can relieve the pressure of vehicle relocation, lowering the relocation fee and reducing the required fleet size. In this study, the operators must determine a mixed vehicle relocation scheme, including operator-based vehicle relocation routes and user-based ride-sharing matches. This problem can be defined as a bi-objective mixed-integer linear programming model to minimize total user fees and maximize system benefits. The linear weighting method can combine those two objectives into one objective. To solve this problem, we propose a meta-heuristic algorithm based on the state-of-the-art hybrid genetic search with adaptive diversity control (HGSADC). The computational results show that the proposed algorithm can produce high-quality solutions within acceptable computing time. We also show that the proposed mixed vehicle relocation strategy can benefit operators and users.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it