Dynamic Capacitated Arc Routing Problem in E-Bike Sharing System: A Monte Carlo Tree Search Approach
Bibliographic record
Abstract
This paper studies a dynamic capacitated arc routing problem for battery replacement in an e-bike sharing system, where workers replace batteries for underpowered e-bikes along street segments dynamically. The objective is to replace as many batteries as possible and minimize pickup failures. The temporal dependency of the routing decisions, the conflict of the workers’ operations, and the stochastic and dynamic nature of user demands all make this a difficult problem. To cope with these difficulties, a “Partition-First, Route-Second” bi-level solution framework is adopted to describe the problem in two different time scales. On the large time scale, a spatiotemporal partitioning method, which divides the road network into nonoverlapping subzones, is proposed to decompose the problem. On the small time scale, this paper models the routing decision process of individual worker as a Markov Decision Process. We adopt a lookahead policy that simulates future information and decisions over some horizons to evaluate the long-term influence of current feasible decisions. A Monte Carlo Tree Search algorithm is also used to improve the simulation efficiency. By performing numerical computation experiments on a test case study and comparing with some benchmarking policies, we demonstrate the effectiveness and efficiency of the suggested method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".