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Logistics
Network Optimization
An applied GeoLayers workflow showing how to move from project evidence to a structured logistics network optimization result.
Browser-based GISGeoAI + spatial analyticsSouth African context
THE DECISION
How should hubs, routes and stops be organised?
Start by defining depots/hubs, service points, demand volumes, vehicle assumptions and whether the objective is shortest distance, lowest travel time, fewer trips or a cost/emissions balance.
NETWORK
Build a realistic travel graph
Use a LineString road layer with segment speed fields and one-way rules where available. Validate connected components so unreachable points are identified before optimisation.
ROUTING
Optimise multi-stop travel
Build an initial stop sequence and improve it using route optimisation. Review per-leg distance and travel time, and include return-to-depot requirements where they reflect real operations.
ALLOCATION
Test hub-and-spoke structure
Allocate spokes or collection points to hubs based on network cost. Use load and vehicle capacity to estimate trips, round-trip kilometres, operating cost and emissions for each hub.
OUTPUT
Compare scenarios before changing operations
Publish route and allocation layers, summarise KPIs by hub, and compare alternative depots, capacities or service territories. The value is not a single "optimal" route but a transparent basis for operational trade-offs.
PROJECT INPUTS
Evidence required for a credible analysis
The quality of the result depends on the source evidence and the assumptions attached to it.
- Road network with speed/one-way attributes where available
- Depot or hub locations
- Collection/delivery/service points
- Demand or load values
- Vehicle capacity and optional cost/emissions assumptions
DECISION OUTPUTS
What the workflow should produce
A useful case-study result should leave the project with evidence that can be inspected, compared and communicated—not only a final map.
- Optimised stop sequence and route legs
- Travel time and route distance
- Hub allocation by spoke
- Estimated trips and round-trip kilometres
- Scenario cost/emissions comparison
INTERPRETATION & LIMITATIONS
Optimisation is only as realistic as the operational assumptions
A route that is mathematically shorter may still be impractical because of loading times, vehicle restrictions, opening hours, road condition or service priorities that are not represented in the network. Treat the model as a scenario engine: compare plausible hubs, capacities, speed assumptions and service territories, then validate the preferred scenario with the operations team before implementation.
- Network speeds and one-way rules reflect the available evidence.
- Unreachable stops are reported rather than silently ignored.
- Vehicle capacity and demand assumptions are visible in trip calculations.
- Alternative scenarios can be compared on distance, time, cost and emissions.
RELATED CAPABILITIES
Recreate the workflow with connected GeoLayers tools
The case study pattern can be adapted to your own project data rather than copied as a fixed recipe.