Route Optimisation Software: Constraints, Scheduling and Real-World Fleet Operations
How route optimisation software works: routing constraints, time windows, capacity, breaks, static vs dynamic routing, solvers vs SaaS and driver adoption.
How does route optimisation work in practice?
Route optimisation assigns jobs to vehicles and sequences them to meet an objective (cost, distance, lateness) within constraints: time windows, capacity, skills, shifts, breaks and depots. Good results depend more on accurate inputs, such as geocoded addresses and realistic service times, and on driver adoption than on the algorithm. Buy a routing product for standard needs; build with a solver when constraints or integration are unusual.
This guide is for logistics, delivery and field service managers. For the wider platform, see logistics software development.
Key takeaways
- Define the objective and constraints explicitly.
- Fix address quality and service time estimates first.
- Plan breaks and shift limits; operators remain responsible for compliance.
- Support re-optimisation during the day.
- Compare planned and actual to improve.
Constraints to model
| Constraint | Example |
|---|---|
| Time windows | Deliver between 7 and 10 am |
| Capacity | Weight, volume, pallets, temperature zones |
| Skills and equipment | Licensed technician, tail-lift truck |
| Shifts and breaks | Start and end times, required rest |
| Depots and returns | Multiple depots, mid-shift reloading |
| Priorities | Urgent jobs, service level agreements |
| Road restrictions | Vehicle size limits, access rules |
Static vs dynamic routing
Static routing plans the day in advance, which suits scheduled deliveries. Dynamic routing re-optimises as jobs are added, cancelled or delayed, which suits on-demand services and field work. Most operations need both: a planned route plus controlled changes during the day, communicated clearly to drivers and customers.
Solvers vs SaaS
| Option | Fits when |
|---|---|
| Routing SaaS | Standard deliveries or visits; fast start |
| Solver library | Unusual constraints or objectives; embedded in your platform (Google OR-Tools) |
| Hybrid | SaaS for routing, custom integration with orders, telematics and customer notifications |
Australian considerations
Long regional distances, limited coverage and remote access make travel-time data and offline driver apps important. For heavy vehicles, plan work and rest in line with fatigue rules under the Heavy Vehicle National Law (NHVR), noting that some states have different arrangements; software supports compliance but does not ensure it. See GIS application development for address and location data.
Hypothetical example: a building supplies distributor
This example is hypothetical. A distributor planned truck runs manually each afternoon. Routes often missed delivery windows because unloading at building sites took longer than assumed. It adopted a routing service integrated with its order system, captured actual unloading times from the driver app, and fed them back as service-time estimates by site type. Planners reviewed suggested routes, drivers flagged site access issues, and planned versus actual times were compared weekly.
Common mistakes
- Optimising with unrealistic service times.
- Ignoring driver knowledge and feedback.
- No re-planning when the day changes.
- Assuming software handles fatigue compliance.
- Measuring kilometres saved but not lateness and customer outcomes.
Related guides
- Field service management software for scheduling technicians.
- Offline-first mobile apps for driver apps.
- Predictive analytics vs generative AI for forecasting demand and service times.
- Integration architecture patterns for connecting orders and telematics.
Conclusion
Route optimisation delivers when constraints are modelled honestly, inputs are accurate, drivers trust the routes and plans adjust during the day. Choose SaaS or a solver based on how unusual your constraints are, and keep comparing planned with actual. For UK waste operators, see digital waste tracking software. For vehicle defects and inspections, see fleet maintenance software in the UK.
Planning routes by hand?
ZSpace Labs is an India-based, remote-first technology studio working with Australian and international businesses on logistics platforms and routing integrations and driver apps. If it would help to model your routing constraints, we are happy to talk it through.
Common questions.
It assigns stops or jobs to vehicles and orders them to minimise cost, distance, time or lateness while respecting constraints such as time windows, vehicle capacity, driver skills, shift lengths and breaks. In optimisation terms it solves a vehicle routing problem.