CASE STUDY // Python · Optimisation
Taxi RouteOptimizer
Python route-optimization tool solving multi-stop taxi routing with real-world constraints.
- Role
- Built the full pipeline: input processing, geocoding, constraint solving, and map output.
- Context
- Personal Python project.
- System Type
- Algorithmic Routing / Optimisation Project
- Status
- Documented
- Priority
- Secondary
- Stack
- Python · pandas · openpyxl · OpenRouteService · Nominatim · Google OR-Tools · Folium
Problem
Multi-stop taxi routing with capacity, pickup/dropoff precedence, and time-window constraints needs constraint-solving, not manual scheduling.
Context
Personal Python project.
Role & Ownership
Built the full pipeline: input processing, geocoding, constraint solving, and map output.
SCOPE CLARIFICATION
This is a focused algorithmic/software project, not a commercial logistics platform. It demonstrates route modelling, constraint-solving with OR-Tools, geocoding with a fallback provider, and implementation discipline — the output is a standalone Python tool with interactive map output.SYSTEM DESIGN
Architecture
Reads Excel input via pandas/openpyxl, geocodes addresses through OpenRouteService with a Nominatim fallback, solves the routing problem with Google OR-Tools under capacity, precedence, and soft time-window constraints, and renders results as an interactive Folium map.
SCOPE NOTE
Standalone Python tool, not a hosted service.
KEY CAPABILITIES
What it does
Excel-based input processing
Geocoding with fallback provider
Capacity constraints, pickup/dropoff precedence, soft time windows via OR-Tools
Interactive Folium map output
TECHNICAL STACK
Technologies used
CAPABILITY PROOF
What this proves
Applied algorithmic/optimization skill and comfort with constraint-solving libraries, distinct from the CRUD-heavy primary projects.
CAPABILITY DOMAINS EVIDENCED
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