Decision Support Systems / 2025
Orphanage Inspection Route Optimization
Modeled inspection routes with service time and time-window constraints, including convergence and route visualization.
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Python - Ant Colony Optimization
- Heuristic Search
- Route Visualization
- Constraint Modeling
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A route optimization project for orphanage inspection activities across Surabaya, Sidoarjo, and Gresik using Ant Colony Optimization.
Optimization practitioner
Modeled inspection routes with service time and time-window constraints, including convergence and route visualization.
Ant Colony Optimization route planning
Context
Inspection routes across Surabaya, Sidoarjo, and Gresik require planning around distance, working hours, and service time at each location.
Problem
The routing problem needed to respect practical constraints: inspection activities should fit within a 09:00-17:00 window or a maximum route duration of around 9 hours, while accounting for service time per location.
My Role
I worked on constraint interpretation, Ant Colony Optimization implementation, route visualization, and evaluation of route efficiency.
Evidence
Approach
- Modeled locations, travel relationships, service time, and route duration constraints.
- Applied Ant Colony Optimization as a heuristic search method.
- Visualized route candidates and iterative convergence behavior.
- Compared route feasibility against the operational time window.
Key Decisions
The project treated the constraints as part of the system, not as afterthoughts. This made the output easier to discuss as an operational planning problem rather than only an algorithm exercise.
Result
The project produced optimized route candidates and convergence visuals that explained how route quality changed across iterations.
What I’d Improve
I would add scenario comparisons for different vehicle counts, buffer time assumptions, and route starting points.
