Modeling, Relocation, and Real-Time Inventory Control of One-Way Electric Cars Sharing Systems in a Stochastic Petri Nets Framework
Bibliographic record
Abstract
In recent years, one-way Electric Car sharing (ECs) systems have been introduced in many cities. One-way trips, as well as battery range issues, directly influence the quality and dynamics of such systems. Due to the demand and supply imbalance at stations, the ECs operators are faced with crucial operational challenges to reduce the relocation costs and increase the number of users. An agent-based relocation strategy based on real-time inventory control within the framework of generalized stochastic Petri Nets (PN) and a discrete event simulation has been proposed in this paper. Furthermore, an associated system performance evaluation was also developed. This model further assesses the effects of system characteristics such as the battery charging level availability threshold on the behavior and dynamics of the system. Moreover, the developed model and simulation show the potential of using PN models to predict critical situations, analyze relocation strategy efficiency, and improve system performance. Results from the simulation indicate that the overall relocation trips are reduced by estimating the time to launch the relocation process, as well as the conflicts between agents (controlling the assignment of agents among stations) during the balancing process are efficiently resolved. The proposed model and simulation algorithm have been applied to the BlueSG network in downtown Singapore.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".