What it means
A delivery van must serve ten customers before their individual closing times, and visiting the nearest next customer each time might miss a later time window. Route optimisation considers the whole set of stops and constraints.
Google's route-optimisation documentation describes time windows for vehicles, pickups and deliveries, and its load-demand guidance treats capacity as another constraint, though these are software concepts, not proof that a proposed route is feasible on the road. Start with accurate addresses, because a wrong unit, gate or loading entrance can waste more time than a small route improvement saves.
Define the objective too, since minimising kilometres, driving time, fleet size, fuel cost or missed promises can produce different plans. Include service time, as a five-minute parcel handoff and a 45-minute installation are not equal stops, and respect customer windows so the solution does not silently break an important commitment to save distance.
Check capacity and matching: weight, volume, refrigerated space, vehicle restrictions, special equipment, qualifications, site access and working-hour rules may all limit which vehicle or crew can serve a stop. Account for depots, since a driver may start at one site, collect stock from another and return elsewhere, and loading order should agree with the route so the first delivery is not buried behind the last.
Consider parking and access restrictions as well, because a low-traffic street may lack legal unloading space and a truck may face height or weight limits. An illustrative distance saving is (original route kilometres - planned route kilometres) / original kilometres, so a move from 120 to 108 kilometres is 10%.
Service, labour and fuel outcomes must be measured separately, and planned must not be confused with actual, since GPS and proof-of-delivery records show what happened and a planned 108-kilometre route may become 130 because of diversions. Historical travel times help with traffic, but incidents and roadworks change conditions, so the plan should allow dynamic adjustment within safe limits.
Plan breaks and safety, because a route with no lawful or practical rest can be unacceptable even if it is shortest, and no one should be pressured into unsafe speeding to meet an algorithm's target. Handle failed stops by giving dispatch a way to revise the route and update customers, and keep the original route and the reason for revision so performance analysis remains honest.
Compare alternatives as a cost-service trade-off, for example whether a second van reduces late deliveries at additional fixed cost, and avoid ranking drivers only by stops per hour, since urban routes, rural travel and bulky orders have different productivity. For owners, route optimisation turns a list of stops into an executable service plan.
Protect route and location data because it can reveal employee movements and customer addresses, test the plan on representative days against on-time service, kilometres and driver feedback, and remember that poor input data can make sophisticated software confidently wrong. Its value comes from accurate constraints and actual results, not an attractive line on a map.
In practice
Real-world examples.
Example
A van sequence respects customer delivery windows instead of choosing only nearest stops. A bakery supplier serves a hotel before its 7 am breakfast deadline even though a nearer cafe lies on the way.
Example
A refrigerated shipment is assigned to a suitable vehicle with enough capacity. The planner checks both temperature range and load volume before the route is released.
Example
Dispatch revises a route after a road closure and tells affected customers. The original plan and the reason for revision are kept so later performance analysis stays honest.
Formula
Calculation
Planned distance reduction = (original route km - planned route km) / original route km x 100. Measure actual outcomes separately.
Worked example: (120 km - 108 km) / 120 km x 100 = 12 / 120 x 100 = 10%. At a fuel and vehicle cost of $0.80 per kilometre, the saving is 12 km x $0.80 = $9.60 per route per day.
Now suppose the plan adds 30 minutes of unloading time at one customer, and driver cost is $24 per hour. The extra labour is 0.5 x $24 = $12, which is more than the $9.60 distance saving, so the shortest route is not the cheapest once service time is counted.Case study
Seen in the real world.
This entirely fictional example follows Silver Couriers. Its software shortened routes but ignored 30-minute unloading at a major customer. After adding service times and access rules, dispatch met more windows despite slightly longer distance. The team compared actual kilometres and on-time deliveries. The case does not imply a single universal optimisation target.
Before the change, Silver planned on map distance alone and averaged late arrivals on roughly one delivery in six. Drivers fixed the plan informally by skipping lunch breaks, which created safety concerns that the old reports never showed. After the change, the planner recorded breaks, unloading times and loading order in the system. Kilometres rose by about 3%, but late deliveries fell sharply and overtime shrank, which the owner judged a better trade.
Watch out
Common mistakes.
- Optimising kilometres while ignoring customer windows and service times.
- Assuming planned routes equal actual travel and delivery performance.
- Overlooking vehicle capacity, load order or driver safety.
Questions
People also ask.
What is route optimisation?
Planning stop sequence and travel against goals and operating constraints.
Is the shortest route always best?
Not necessarily. It may miss windows, capacity or service requirements.
How should route changes be judged?
Compare actual costs, distance, service outcomes and driver feasibility.
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