Optimal Fleet Policy of Rental Vehicles with Relocation: A Simulation Study
Bibliographic record
Abstract
Popularity of one‐way car rentals poses a challenge to rental car fleet management and brings to focus the importance of a strategic decision for rental car operators: whether to implement a single‐fleet or a multifleet model. The single‐fleet model allows movement of vehicles between regions, whereas the multifleet model does not. It is not obvious whether a single‐fleet model is optimal due to its pooling effect, or a multifleet model due to shorter car relocation times. In practice, different rental car operators use different models. To answer this conundrum, we develop two simulation models and compare them in terms of fleet utilisation, branch service level, relocations, and operating profit. We have taken the New Zealand rental car industry as an example as the country consists of two well‐defined regions: the North Island and the South Island. The results indicate that a multifleet model has a higher service level at key centres and higher utilisation. At the same time, the single‐fleet model is relatively more profitable at the expense of a lower service level in key centres due to vehicles accumulating in the South Island due to a significant volume of one‐way southbound travel. Overall, the implementation of either model should depend on the strategic goals of the rental car operator. Our work will be useful for practitioners considering whether or not to pool their fleet when allowing for one‐way rentals with subsequent relocation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".