Evaluating car sharing fleet management strategies using Discrete Event Simulation
Bibliographic record
Abstract
Evaluating car sharing fleet management strategies using Discrete Event Simulation \nAlfred John Chellanthara \nConcordia University \nDynamic fleet management is often faced with the problem of managing real time customer requirements and unforeseen events that affect the performance of transport operations. In a car sharing organization, vehicle availability is considered as a measure of quality of service, which is defined by the availability of a car at the time when the user arrives at the station. This thesis presents a decision support tool in order to test the efficiency of a round trip (return to same station) model as compared to a one way (return to any station) model for fleet management in a car sharing organization. The proposed tool employs a discrete event simulation (DES) model which evaluates rejection rate for each of the individual strategies and recommends the one with least number of rejections. A case study is conducted on the CommunAuto car sharing network of Montreal. The results show that the one way model has a greater request rejection rate with an average rejection rate of 13%, while the round trip model has an average rejection rate of 8%. The utilization rate of the round trip model is much higher with 92% utilization as compared to the one way model which has a utilization rate of 87%. Therefore, the round trip model is recommended to CommunAuto for managing its current fleet operations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".