Optimize last-mile ops: cost per delivery, breakeven density, fleet utilization and routing scenarios. Daganzo model, AI, 3 scenarios. Free.
Advanced simulator
Which zone is losing money today, and what do I change first?
Spot which route or fleet costs more than it earns, and which density or vehicle change turns the operation profitable.
Fleet and drivers
Vehicles, shift hours and direct daily operating costs.
Zone and timing
Delivery-zone geography and operational timing.
Daganzo constant (k)
Geometric shape of the routing area. 0.55 clustered zones (LatAm urban neighborhoods), 0.70 random tour (Daganzo 1984), 0.85+ sparse rural. Scales the mean inter-stop distance linearly.
k = 0.62
Revenue and fuel
Revenue per delivery, fuel variable costs and operating month.
Saved configurations
Fill in your data to see the report
This simulator only generates a diagnosis, charts and recommendations when it has your real business values. Fill the editor above and the report will appear automatically.
Required data
Available vehicles
Vehicle cost per day
Driver cost per hour
Fuel price
Revenue per delivery
Deliveries per day
Want to explore it first?
Load a realistic case to see how the report looks. You can edit any field afterwards.
How results are calculated, what we assume when modeling, and where the method loses precision.
Formula
Total cost = Σ Distance × Cost/km + Time × Cost/h + SLA penalty · Cost per delivery = Total cost ÷ Deliveries
Assumptions
Average speed constant per zone (urban / suburban).
Per-stop service time (drop time) stable.
Per-vehicle capacity respected as a hard constraint.
Applicability limits
Heuristic optimization — does not guarantee the global optimum of the Vehicle Routing Problem.
Strict time windows and multi-depot are not modeled.
Traffic treated as a seasonal factor, not real-time.
Sources
Dantzig & Ramser (1959) — The Truck Dispatching Problem (Vehicle Routing).
Internal editorial estimate based on industry best practices.
How it works
1. Define your fleet
Vehicles, cost per day, driver cost, shift hours and physical capacity per vehicle.
2. Characterize the zone
Density (stops/km²), service time, effective speed, success rate. Realistic values per urban zone type.
3. Compare scenarios
Base, tough-zone expansion (lower density) and optimized routing. Identify breakeven and profitable quadrants.
Frequently asked questions
1How do you calculate average distance between stops?
Uses the Daganzo approximation for quasi-random routing zones: avg_distance ≈ 0.7/√density. Standard logistics heuristic to estimate tours without solving full VRP. For zones with peculiar topology (islands, rivers, limited roads), actual distance may differ.
2What is breakeven density?
It is the stops-per-km² density at which margin per delivery reaches zero. Below that number, operating is a net loss. Computed via binary search while keeping other parameters constant. Useful for deciding geographic expansion.
3Does it consider waiting times or delivery windows?
Not directly. The model assumes continuous flow during the shift. If your operation has strict windows (e.g., 8-10am for a client), real utilization will be lower. Use it as an upper bound on capacity.
4Does it work for courier, food delivery and e-commerce equally?
Principles are the same but tune: service time (food delivery 2-3 min, B2B courier 8-12 min), success rate (food delivery 98%+, residential courier 80-90%), revenue per drop (food delivery USD 4-8, B2B courier USD 6-15). The model is flexible to these ranges.
VRP is the extension of the Traveling Salesman Problem (TSP) when there are multiple vehicles, different capacities, customer time windows, and constraints like maximum stops or route time. It's NP-hard, so it's not solved by enumeration but by heuristics (Clarke-Wright savings, 2-opt) or specialized solvers like Google OR-Tools, Routific or Optibus, which reach solutions within 2-5% of the theoretical optimum in seconds for real 200-500 stop instances.
2How much can I save by optimizing routes?
The ARC Advisory Group and DHL Supply Chain benchmark is 15-30% reduction in transport cost when migrating from manual planning to a VRP solver. Range depends on starting inefficiency: operations already using fixed zones and milk runs gain 10-15%; those running dispatcher-driven manual planning frequently recover 25-30%. Software ROI (OR-Tools open-source + integration) typically pays back in 1-3 months.
3What is route density and why does it matter?
Route density = stops per route divided by kilometers driven. It's the central metric for marginal delivery cost: higher density, lower cost per stop. US last-mile urban benchmarks are 2-5 stops/km; UPS in Manhattan and dense LatAm zones exceed 6 stops/km. Improving route density 20-30% typically reduces cost per stop 15-25%.
4How do I handle customer time windows?
Time windows are hard VRP constraints. A B2B customer receiving only 9-11am turns its stop into a constraint: if you miss, failed delivery (1.5-3x original cost). The solver weighs window violation penalty against marginal detour cost. Operating discipline: tight windows during morning rush (store openings) justify more vehicles; wide windows let you consolidate into fewer routes.
5What is backhaul optimization?
Backhaul means using the empty return leg for return cargo or pickups. A truck that runs empty 30% of the time has cost efficiency 40-60% worse. Tactics: milk runs with B2B pickup after B2C delivery, scheduled returns on the way back to DC, consolidation with another operator at cross-dock. Impact on cost per kilometer can be 20-35% improvement.
6Is OR-Tools free?
Yes, Google OR-Tools is open-source (Apache 2.0) and includes solvers for CP-SAT, linear programming, routing (VRP/TSP) and assignment. Real cost isn't license but integration: an engineer with optimization experience gets a first version running in 2-4 weeks. Commercial alternatives with built-in UI: Routific (~USD 49/vehicle/month), Circuit for Teams (~USD 20/driver/month), Onfleet (starting USD 500/month).
7Does Google Maps work for route optimization?
Partially. Google Maps Directions API optimizes waypoint sequence (TSP up to 25 stops) but doesn't solve VRP — it ignores multiple vehicles, capacities, time windows and shift constraints. For delivery with more than 15 stops per vehicle or multiple vehicles, you need a real VRP solver (OR-Tools, Routific, Optibus).
What you'll see, what it prevents, and where you shouldn't trust it
Every simulator on Simúlalo ships with the same editorial structure: two hypothetical worked examples with numbers, the errors it helps you avoid, the model's declared limitations, and a visible financial disclaimer. The review is signed and dated.
Hypothetical case·Case A
An operation that finds out its minimum profitable density is 9 deliveries/hour, not 6
A food delivery company had 4 motorcycles at 6 deliveries/hour with a target unit cost of $32 MXN/delivery. The simulator, with rider fixed cost ($14,000/mo), fuel, and depreciation, computes a minimum profitable density of 9.2 deliveries/hour — not 6. At 6 deliveries/hour, the real cost per delivery is $54.80. The decision: cut fleet from 4 to 3 motorcycles in off-peak hours (density rises), outsource peak hours (high density absorbed by in-house fleet), and monitor conversion for 30 days.
Illustrative figures. Does not represent a real company or an investment recommendation.
Hypothetical case·Case B
A retailer that outsources peripheral zones where in-house costs 38% more
A retailer delivers in CDMX with an in-house fleet. The simulator breaks down cost by zone: central $24/delivery (density 12), peripheral $74/delivery (density 3.2). The 3PL offer is $58/delivery with the same SLA. The gap: $16/delivery × 1,200 deliveries/mo in periphery = $19,200/mo in savings. The decision: outsource periphery, keep the in-house fleet in central zones where density sustains the cost. Reassign two underused central routes to same-day deliveries.
Illustrative figures. Does not represent a real company or an investment recommendation.
Common mistakes it helps you avoid
Things a team or decision-maker might assume that this simulator forces you to verify before committing.
Calculating cost per delivery using fuel only: you need to add rider, depreciation, maintenance, vehicle financing cost, and accident rate.
Comparing 3PL versus in-house without an equivalent SLA: if the 3PL has 2x delivery time, conversion drops and the savings evaporate.
Assuming uniform density: density varies by hour, day of week, and zone — the simulator forces you to declare it per segment for accurate cost.
Ignoring return cost: low FTR (first-time-right) drives re-delivery and doubles cost in problem zones.
Model limitations
What the simulator does not do, and where you need a professional or a specialized tool.
Does not optimize routing in real time (does not replace Google OR-tools, OptimoRoute, or similar). It models cost from declared densities.
Assumes a standard shift and regular operating regime. For peak seasons (Hot Sale, Black Friday) you need to model separately.
Does not include externalities: extreme traffic, mass events, natural disasters. When they happen, real cost spikes and assumptions lose validity.
Density break-even assumes you can adjust fleet. If headcount is locked by contract, cost per delivery changes.
When NOT to use this simulator
If your business is high-cadence B2C last mile with a rider incentive structure (gig model), unit cost depends heavily on tip mechanics, sub-contracting, and per-kilometer commission — variables a deterministic simulator does not capture cleanly. In that case, model the fixed component of your headcount first and treat the variable as pass-through to the service price.
Financial notice
Results are illustrative estimates and do not constitute financial, tax, accounting, or legal advice. Use the results as a reference point and validate important decisions with a certified professional.
Editorial review
Reviewed by the Simúlalo editorial team
This simulator was reviewed by the people listed below before being published. The review covers the declared formula, the model's assumptions, the explicit limitations, and the absence of unsupported financial claims.
They are part of the Simúlalo editorial team, focused on building financial tools that are clear, educational, and easy to interpret.
Last updated: ·We update this page when the methodology, sources used, or simulator structure change.
This tool uses standard financial formulas and user-supplied data. To explain concepts like rates, credit, risk, or cash flow we consult public and official sources (Banxico, SAT, CONDUSEF, CNBV, Banco de España, IFRS, BIS, among others). Simúlalo is not affiliated with, sponsored by, or endorsed by these institutions.