Reduces the number of EVs needed to meet demand with optimal vehicle allocation, scheduling, and route switching.
EVOROUTE
Electric Vehicle Optimization Routing Engine
Optimize Your Electric Fleet with Fewer Vehicles
EVOROUTE analyzes trip schedules, depot and charging point locations, and vehicle specifications to generate efficient routes and schedules for each vehicle—reducing the required fleet size while maintaining your original service levels.
Vilnius GTFS transit data is used as the input source.
An example of an optimized route with route switching disabled.
Why Fleet Operators Should Choose EVOROUTE
Accounts for battery capacity, discharge per km, and charge rates to ensure optimal deployment.
Outputs trip schedules per vehicle — for weekdays, weekends, and seasonal changes.
Allows selection between fast heuristic optimization for near real-time on-demand vehicle dispatching and exact solver-based optimization for globally optimal scheduling.
Models routing over real-world road networks using travel times that account for speed limits and road geometry.
Assigns charging windows and locations based on vehicle state-of-charge, charger availability, and operational schedules.
Trusted by Leading Lithuanian Transport Company
How It Works
Our optimization engine transforms client's transit data into efficient electric vehicle schedules. It minimizes fleet size while ensuring full coverage.
- Stops & Routes: Ordered lists of locations vehicles have to visit
- Trips: Arrival and departure timetables
- Service Schedules: Weekday, weekend, holiday, seasonal or other trip groupings
- Vehicle Specifications: Battery capacity, charging and discharging rates
- Depots and Charging Points: Parking and charging locations
- Minimal Fleet Size: Minimum number of vehicles required to meet demand
- Vehicle Schedules: Individual vehicle schedules generated from input service schedules
- Multi-Location Charging Schedules: Charging schedules for each charging point location
- Route Switching Vehicles (if enabled): Vehicles performing swaps between compatible trips in different routes
- Near Real-time Mode
- Graph-based heuristics for near real-time planning.
- Globally Optimal Mode
- Exact fleet size minimization using linear programming.
graph LR
%% Inputs
subgraph Ingest ["Data Inputs"]
I1["Stops & Routes"]
I2["Trips"]
I3["Service Schedules"]
I4["Vehicle Specifications"]
I5["Depots & Charging Points"]
end
%% Engine Box
Engine["Optimization Engine"]
%% Internal Modes
subgraph Modes ["Solver Modes"]
FM["Real-time Mode"]
OM["Optimal Mode"]
end
%% Outputs
subgraph Results ["Optimized Outputs"]
O1["Fleet Size"]
O2["Individual Vehicle Schedules"]
O3["Charging Point Schedules"]
end
%% Relations
I1 --> Engine
I2 --> Engine
I3 --> Engine
I4 --> Engine
I5 --> Engine
Engine --> FM
Engine --> OM
FM --> O1
FM --> O2
FM --> O3
OM --> O1
OM --> O2
OM --> O3
class Engine important
Estimate Your Fleet's Optimization Potential
Input your basic fleet metrics to see how much EVOROUTE could reduce your vehicle requirements and operating costs.
Your Metrics
Estimated Output
Estimated Savings
€225,000
Approximate annual operational cost reduction.
Optimized Fleet Size
25
(5 vehicles saved)
Utilization Efficiency
+16.7%
Higher path coverage per asset
About Us
We specialize in mathematical modeling, operations research, and software engineering.
Daumilas Ardickas
Mathematician
daumilas@evoroute.eu
Mindaugas Kepalas, PhD
Mathematician
mindaugas.kepalas@evoroute.eu
Mindaugas Šukys
Software Engineer
mindaugas.sukys@evoroute.eu
Request an Optimization Demo
Get in touch to run a custom pilot analysis on your fleet dataset