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fleetcharge-solver-config
alirezaaminzadeh/fleetcharge-solver-config
fleetcharge-solver-config is a machine learning model from alirezaaminzadeh. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Reference configuration for the FleetCharge electric fleet dispatch and charging optimization engine.
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At a glance
- Model type
- fleetcharge-solver-config
- Access
- Public
- Created
- Aug 4, 2026
- Updated
- Aug 4, 2026
- SHA
- fb831028
Model card
From the Hugging Face model README
FleetCharge Solver Configuration
Reference configuration for the FleetCharge electric fleet dispatch and charging optimization engine.
Algorithms
| ID | Method | Level |
|---|---|---|
integrated_evrp | OR-Tools EVRP + greedy charging | Operational |
cpsat_charging | CP-SAT queue-aware charging | Operational |
alns_fleet | Adaptive Large Neighborhood Search | Operational |
label_setting | Energy-constrained shortest path | Operational |
queue_aware | Availability-weighted charging | Operational |
strategic_bayesian | Bayesian optimization (EI) | Strategic |
simpy_simulation | SimPy discrete-event validation | Simulation |
Battery Model
- Non-linear charging curve with SOC taper above 80%
- Temperature-dependent energy consumption
- Degradation cost by charger type (slow / fast / ultra-fast)
Usage
from fleetcharge.engine import FleetChargeEngine
from fleetcharge.generator import generate_problem
problem = generate_problem("last_mile_delivery", "baseline")
engine = FleetChargeEngine()
result = engine.optimize(problem, "integrated_evrp")
- Type
- fleetcharge-solver-config
- Created
- Aug 4, 2026
- Updated
- Aug 4, 2026