Datasets:

alirezaaminzadeh
/

fleetcharge-sample-scenarios

benchmarks list	solver_metadata dict
[ { "industry": "last_mile_delivery", "stress": "baseline", "strategy": "integrated_evrp", "on_time_pct": 100, "total_distance_km": 100.7, "total_energy_kwh": 23.2, "electricity_cost": 0, "avg_queue_wait_min": 0, "solve_time_sec": 0.13854179996997118 }, { "industry": "ride_...	{ "optimization": [ "integrated_evrp", "cpsat_charging", "alns_fleet", "label_setting", "queue_aware", "strategic_bayesian", "simpy_simulation" ], "simulation": "simpy_discrete_event", "routing": "or_tools_evrp", "strategic": "bayesian_expected_improvement" }

FleetCharge Sample Scenarios

Synthetic electric fleet dispatch and charging optimization scenarios across 6 industries.

File	Industry	Stress	Algorithm
`sample_last_mile_delivery.json`	Last-Mile Delivery	Baseline	Integrated EVRP
`sample_ride_hailing.json`	Ride-Hailing	Peak Demand	ALNS
`sample_employee_shuttle.json`	Employee Shuttle	Cold Weather	CP-SAT
`sample_urban_transit.json`	Urban Transit	Grid Peak Pricing	Queue-Aware
`sample_industrial_logistics.json`	Industrial Logistics	Charger Outage	Bayesian Design
`sample_cold_chain.json`	Cold Chain	Dynamic Requests	SimPy

Each sample contains:

import json
with open("sample_last_mile_delivery.json") as f:
    data = json.load(f)
print(data["optimization"]["metrics"])