Operational software
Security patrol route optimisation
Operational software
Project outcome
30% less overlap and idle time
A practical route-planning tool that used operational constraints and mapping data to reduce duplicated travel and idle time across security patrols.
Where SSH Tech sat in the system
Signal path
2/6 layers built at SSH Tech
L1
Operation
Patrol vehicles, guards and coverage obligations.
Existing
L2
Inputs
Site locations, timing windows and mapping data.
Integrated
L3
Embedded
No embedded layer was required for this system.
Existing
L4
Compute
Python service modelling routes and constraints.
Built at SSH Tech
L5
Decision
Optimisation of coverage, travel and idle time.
Built at SSH Tech
L6
Action
Route plans returned to coordinators and teams.
Integrated
Discipline load
Mechanical
Electrical
Embedded
Software
Operations
The system problem
Challenge
Patrol routes were being coordinated without a systematic way to account for travel time, coverage requirements and overlap between teams. The cost was duplicated movement, avoidable idle periods and less predictable field coverage.
Our responsibility
SSH Tech translated the scheduling problem into an optimisation model, connected it to Google Maps data, and built a Python tool that could generate routes the operating team could understand and use.
How we worked
- Mapped the real planning rules, required stops and operational constraints before selecting an optimisation method.
- Used travel-time and distance data from Google Maps to replace assumptions with a usable network model.
- Designed the objective around overlap and idle time rather than shortest distance alone.
- Returned routes in an operator-readable form so the result could be reviewed before field use.
Delivery path
Hardest risk first.
Phase 1
Model the operation
Converted patrol coverage, timing and team constraints into a solvable planning problem.
- Route and stop representation
- Coverage and scheduling constraints
- Baseline overlap and idle-time analysis
Phase 2
Build the optimiser
Combined Python optimisation logic with mapping data to evaluate and improve candidate routes.
- Google Maps travel data
- Route-scoring and optimisation logic
- Constraint-aware plan generation
Phase 3
Make the output operational
Presented the proposed plans with enough context for a coordinator to inspect and use them.
- Readable route outputs
- Comparison against the existing plan
- Iteration around real operating feedback
What changed in the operation
30%
Less overlap and idle time
The route planner reduced combined overlap and idle time by 30%.
Constraint-aware
Planning method
Routes accounted for operating rules and travel data rather than distance alone.
Technical stack
Python
Google Maps data
Route optimisation
Operational modelling
Capabilities applied
Optimisation
Workflow analysis
Decision tooling
Operational software
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