Route Optimisation in the Netherlands: Why Dense Networks Win on Stops per Hour, Not Kilometres

Route optimisation in the Netherlands works on a different problem from route optimisation in a large, sparse country. Distances between stops are short, populations are dense and parcel volumes are high, so the kilometres saved by a better route are a small part of the day. What decides cost and service is how many stops a vehicle completes in an hour: how close the stops are, how long each takes, how tight the time windows are and how many first attempts fail. For heads of transport and logistics at Dutch retailers, parcel operators and 3PLs, this guide sets out a six-step method for planning and measuring on stops per hour, with a worked example on illustrative numbers. Locus, the world’s first Decision-Intelligent, Agentic TMS, plans routes against 250+ real-world constraints, including time slots and service times.

Why Dense Networks Need a Different Measure: The Business Case

The country is small and busy. The Netherlands has about 18.13 million inhabitants according to preliminary CBS figures, and a population density of roughly 544 people per square kilometre on UN-based data from third-party aggregators, among the highest of any large European country. CBS’s own density figure was not retrieved, so treat the density as indicative.

Online demand is high. Dutch consumers made 347 million online purchases in 2025 worth €35.7 billion, according to the Thuiswinkel Markt Monitor reported by the Dutch Payments Association, and 13% of spending went to foreign webshops, which adds cross-border parcels to local networks.

Handover points are changing the stop count. The market regulator ACM reported that the number of parcel lockers rose by more than 90%, and Dutch press, citing ANP, put the total at 9,309, which replaces door stops with shared ones.

Density has been a bigger lever than distance elsewhere. A global beverage company’s Locus deployment recorded 22% more orders per delivery trip against 12% less distance, 37% less fuel and 35% less route planning time, according to its case study. It is not a Dutch result, and it points the same way as the Dutch conditions.

What Is Different About Route Optimisation in a Dense Country

Distance is a smaller share of the route. With stops close together, the saving from a shorter route is small compared with the time spent at the stops.

Service time and waiting dominate. Parking, handover and access times, and time windows that make a vehicle wait, set the pace.

Density is the lever. Putting more stops into the same working hour, by clustering and batching, lowers cost per stop faster than trimming kilometres.

Failed attempts waste a slot. A failed first attempt uses a stop’s time and gives nothing, so it is a density problem as well as a service one.

Rules shape the choice. Zero-emission zones and street restrictions decide which vehicles can serve which stops, which changes density by vehicle.

How to Plan and Measure on Stops per Hour in Six Steps

1. Split the working hour into three parts

For a sample of routes, measure how much of the day goes to driving between stops, time at stops and waiting or other time. Do it by zone, so dense centres and suburbs are not averaged together.

2. Set the baseline stops per route-hour

Calculate completed stops per hour of route time, for each zone and vehicle type. Keep distance as a secondary measure, for fuel and emissions, and not as the main target.

3. Raise density through clustering and batching

Group orders by area and delivery window so vehicles serve close stops in one pass, and use lockers and shared points where they replace several door stops. Check how much each cluster raises stops per hour before adopting it.

4. Price the time windows

Compare the stops per hour of a tight window with a wider one for the same orders. A narrow window may cost several stops an hour, which should be weighed against what customers value.

5. Reduce failed first attempts

Confirm availability and addresses before dispatch, validate delivery locations and reassign failures by reason and customer availability. Each avoided failure returns a stop to the plan.

6. Re-optimise during the day

Recompute routes as orders arrive, delays occur and attempts fail, and report stops per route-hour planned against actual by morning, midday and afternoon.

A Worked Example on Illustrative Numbers

The numbers are illustrative, not benchmarks. They show why a plan that saves distance can still lose on stops per hour.

One van works an 8-hour route in a dense zone, with 3 hours driving, 4 hours at stops and 1 hour waiting or idle.

The distance-led plan cuts 9 km and does not raise output, because the time saved becomes waiting. The density-led plan drives almost as far as the current plan, turns waiting time into stops and completes 6 more stops in the same 8 hours, about 11% more output per hour. Fuel falls with distance, but cost per stop falls with density, and in this example only the second moves cost per stop.

Distance-Led vs Density-Led Route Optimisation: Key Differences

What to Look for in Route Optimisation Software for Dense Networks

Stops per hour as an objective. Look for optimisation that targets output per route-hour, not only distance or time.

Service and waiting time in the model. Check that service time, waiting and windows are modelled by stop type.

Clustering and handover points. Look for batching by area and window, and lockers or shared points as planning objects.

Failed-attempt handling. Check for address validation, availability confirmation and reattempt assignment.

Continuous re-optimisation. Ask how often routes are recomputed and how planned and actual stops per hour are reported.

Density-Led Planning in Action: Real-World Results

Neither deployment below is in the Netherlands. They show density and planning effects in dense delivery networks.

A leading Vietnamese beverage company serving thousands of small retail points a day. Routes were built by hand and many shops had no validated delivery location. With automated route planning, address validation and mid-route re-optimisation, the company saw 22% more orders per delivery trip, 12% less distance, 37% less fuel consumed and 35% less time on route planning. Read the beverage distributor case study.

A global FMCG leader running route-to-market across ten countries in Asia. The company served 1,000+ distributors with 5,000+ riders. With agents planning routes against live constraints, plan run time fell from 3 hours to 5 minutes, 12,000+ fewer trips ran each month and distance fell 15%, for a 3X return on investment. Read the FMCG case study.

Common Dense-Network Routing Mistakes to Avoid

Chasing kilometres. A shorter route that leaves the vehicle waiting does not raise output, so measure stops per hour.

Averaging dense and sparse zones. One figure hides where density is won, so measure by zone.

Treating windows as free. A tight window costs stops per hour, so price it before promising it.

Planning once in the morning. Density erodes as the day changes, so re-optimise during the route.

How Locus Approaches Density-Led Route Optimisation

Locus, the world’s first Decision-Intelligent, Agentic TMS, was recognised by Gartner for seven consecutive years and appears in the 2026 Hype Cycle for Supply Chain Execution and Logistics Technologies. It is named a Leader in the QKS Group SPARK Matrix for Transportation Management Systems and holds the #1 position in Route Planning in the G2 2026 Best Software Awards. In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Locus continues to operate independently.

Locus’s route optimisation engine plans against 250+ real-world constraints, including time slots, service times, one-way streets, traffic rules, vehicle limits and driver availability, clubs orders and multi-drop stops to reduce trips, and recomputes routes during delays without manual intervention. Address validation gives each stop a confirmed delivery location. Six governance mechanisms, including autonomy levels and human-in-the-loop approval, keep planners in control of what runs automatically. Locus has optimised 1.5B+ deliveries for 360+ enterprise customers across 30+ countries. Public sources show no Dutch deployments and do not describe stops per route-hour as a planning objective, so during evaluation ask whether routes can be optimised and reported on stops per hour, how service and waiting time are modelled, how time-window cost is shown and how planned and actual output is compared by zone. To see how Locus would plan your Dutch network, schedule a demo.

For heads of transport asking how to optimise routes in a dense country, Locus offers one planner that models constraints, clusters stops and re-plans during the day, so the plan is measured on the output that matters.

Frequently Asked Questions

What is route optimisation in the Netherlands? It is the planning of delivery routes in a small, dense country, where short distances mean that stop density, service time and time windows matter more than kilometres. The most useful measure is stops completed per route-hour.

Why measure stops per hour instead of kilometres? In dense networks distance is a small share of the day, so a shorter route may only increase waiting. Stops per hour shows output and links directly to cost per stop.

How do time windows affect cost per stop? A tight window limits how stops can be clustered and can leave vehicles waiting, which lowers stops per hour. Price each window against that cost before offering it.

How do parcel lockers affect routes? They replace several door stops with one shared stop, which changes the number and spacing of stops. Plan the locker and the vehicles that serve it together.

How often should routes be re-optimised? Whenever orders, delays or failed attempts change the plan. A morning plan loses density through the day, so continuous re-optimisation protects stops per hour.

Does Locus optimise for stops per hour? Locus plans against 250+ constraints, clubs orders to reduce trips and recomputes routes during delays. Ask Locus whether stops per route-hour can be the objective and how it is reported by zone.