TRANSFORMING PARKING FACILITIES TO ACCOMMODATE ELECTRIC VEHICLES: A CAPACITATED MULTI-FACILITY LOCATION PROBLEM APPROACH.
Bibliographic record
Abstract
This study addresses the limitations of Electric Vehicles Charging Stations (EVCS) in Montréal, Québec. Momentum, the growth of Electric Vehicles (EVs) is projected to accelerate substantially. However, this growth is hindered by limited EVCS, particularly in shopping center settings where high cost and spatial limitations pose significant challenges. This study addresses these gaps by proposing an optimization model to support cost-effective EVCS placement. The problem employs Mixed-Integer Nonlinear Programming (MINLP), categorizing it as a capacitated multi-facility location allocation challenge. This approach is designed to minimize the total lifecycle costs for property owners, including costs related to equipment, electrical system equipment, operational and maintenance, fixed costs, and dismantling expenses. The optimization process involves determining the desired number of ports in the parking, based on the available estimation of the EV growth as a first step. Afterward, determining parking blocks based on the parking size to allocate the different types of ports into them. Then, various port type combinations, are distributed into all the generated block combinations. The proposed formulation is a Life Cycle Cost (LCC)-Based optimization designed to achieve minimum costs. The solution introduces a combinatorial optimization algorithm that combines dynamic programming and brute-force search to ensure all potential configurations are considered. In conclusion, this study provides a framework for shopping center owners to adapt their parking facilities to EVs. The proposed cost formulation provides a financially sustainable solution for EVCS placements. Through this approach, this study offers a practical method to enhance EV infrastructure within commercial indoor parking environments, balancing financial and logistical considerations
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".