alirezaaminzadeh/fleetcharge-solver-config

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")