🚚 Route Optimization for Goods Distribution to Retail Stores (1)
A distribution company needed to efficiently deliver goods from a single distribution center to 25 grocery stores using a fleet of three trucks. Each store had specific demand quantities and time windows for deliveries, while each truck had limited capacity. The challenge was to design optimal delivery routes that would minimize overall transportation costs while respecting all operational constraints.
A distribution company needed to efficiently deliver goods from a single distribution center to 25 grocery stores using a fleet of three trucks. Each store had specific demand quantities and time windows for deliveries, while each truck had limited capacity. The challenge was to design optimal delivery routes that would minimize overall transportation costs while respecting all operational constraints.
Objective
My objective was to model and solve a vehicle routing problem (VRP) using ArcGIS Pro to create an optimized delivery plan that would assign stores to trucks, sequence deliveries efficiently, and generate turn-by-turn directions for drivers.
Approach
I implemented a structured approach using ArcGIS Pro's Network Analyst extension:
Spatial Data Configuration
- Geocoded all 25 store locations and the distribution center
- Prepared the road network dataset for routing analysis
Operational Constraints Setup
- Configured orders with specific demands, delivery time windows, and service times
- Set up three routes with capacity constraints (15,000 lbs each)
- Defined time limits (6-hour shifts maximum, 2-hour maximum continuous drive time)
- Established distance restrictions (80 miles maximum per route)
Cost Parameters Definition
- Labor cost: $0.20 per minute
- Vehicle operation cost: $1.50 per mile
- Configured travel settings using "Driving Time" mode
Optimization and Output Generation
- Executed the VRP solver for mathematical route optimization
- Generated detailed turn-by-turn directions for each route
Results
The VRP solver produced three optimized delivery routes that achieved:
✅ Complete Coverage - All 25 stores efficiently served within operational constraints
✅ Balanced Workload - Optimal distribution of deliveries across the fleet while minimizing total costs
✅ Constraint Compliance - All time windows and capacity limitations fully respected
✅ Actionable Directions - Automated generation of detailed turn-by-turn instructions for drivers
✅ Significant Savings - Mathematical optimization demonstrated substantial cost savings compared to manual planning
Impact
This project demonstrates how spatial optimization techniques and operations research algorithms can transform distribution logistics:
- Reduced operational transportation costs
- Improved delivery efficiency
- Better fleet utilization
- Enhanced customer satisfaction through time window compliance
Technologies Used:
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