Charging Pile Siting with Group Multirole Assignment
Bibliographic record
Abstract
Oil resources are becoming increasingly scarce. Pure electric vehicles have huge advantages, in terms of energy efficiency and emission reduction. In cities, the locations of required charging stations and the number of required charging piles are determined according to the traffic flow on a road. Unreasonable allocation not only creates safety problems due to high electrical loads, but also increases the cost of the placements. Such allocations will involve the many-to-many (M2M) assignment in the process, which is necessary to establish an optimal model for distributing. Thus, this paper formalizes the charging pile siting problem (CPSP) via the group multirole assignment (GMRA) model, which is one of the most important methods to deal with the M2M problem. Based on GMRA, this paper proposes a role negotiation method by using a spectral clustering K-Means++ Algorithm based on location. The formalization of GMRA makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX) via the Integer Programming (IP). All the proposed approaches are verified by simulation experiments, which have been proved to be efficient, feasible and practicable.
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.
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 | 0.000 |
| 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".