An Incentive-Compatible Combinatorial Auction Design for Charging Network Scheduling of Battery Electric Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Charging network scheduling for battery electric vehicles is a challenging research issue on deciding where and when to activate users’ charging under the constraints imposed by their time availability and energy demands, as well as the limited available capacities provided by the charging stations. Moreover, users’ strategic behaviors and untruthful revelation on their real preferences on charging schedules pose additional challenges to efficiently coordinate their charging in a market setting, where users are reasonably modelled as self-interested agents who strive to maximize their own utilities rather than the system-wide efficiency. To tackle these challenges, we propose an incentive-compatible combinatorial auction for charging network scheduling in a decentralized environment. In such a structured framework, users can bid for their preferred destination and charging time at different stations, and the scheduling specific problem solving structure is also embedded into the winner determination model to coordinate the charging at multiple stations. The objective is to maximize the social welfare across all users which is represented by their total values of scheduled finishing time. The Vickrey–Clarke–Groves payment rule is adopted to incentivize users to truthfully disclose their true preferences as a weakly dominant strategy. Moreover, the proposed auction is proved to be individually rational and weakly budget balanced through an extensive game-theoretical analysis. We also present a case study to demonstrate its applicability to real-world charging reservation scenarios using the charging network data from Manhattan, New York City.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it