Case Study
Restaurant Delivery Suite
An intelligent delivery management system optimizing routes for hundreds of riders in real-time, reducing delivery times and fuel costs.
The Challenge
Understanding the Problem
Our Approach
The Solution
Google Maps Directions API with a custom Dijkstra-based algorithm for shortest delivery paths. Real-time order batching for multi-stop efficiency.
- -25% Delivery Time
- -30% Fuel Costs
- 200+ Active Riders
The Outcome
Reduced average delivery time by 25% and fuel costs by 30% while improving rider satisfaction with optimized routes and fair order distribution.
Impact: 25% Faster Deliveries
Technical Deep Dive
Engineering Excellence
A comprehensive look at the technical architecture and implementation details that power this solution.
tracking
Battery-optimized background location tracking with Kalman filter smoothing for accurate real-time position updates without excessive battery drain.
scale
Event-driven architecture capable of handling high-volume order bursts during peak lunch and dinner hours. Auto-scaling to 10x normal capacity.
System Architecture
Rider App
React Native
Socket.io Server
Real-time Events
Route Optimizer
Dijkstra Algorithm
Google Maps API
Traffic + Directions
MongoDB
Orders + Routes
Development Journey
From Concept to Launch
Logistics Analysis
Mapped delivery zones with geofencing polygons for accurate ETAs, analyzed historical pattern data to identify peak hours and traffic congestion hotspots, and interviewed riders to understand pain points in current routing and order assignment workflows.
Routing Engine
Implemented Dijkstra's algorithm variant optimized for real-time traffic data from Google Maps Directions API, built dynamic order batching system that groups nearby deliveries for multi-stop efficiency, and developed fair load balancing algorithm to distribute orders equitably among available riders.
Rider App
Created React Native app with offline map caching using react-native-maps-offline, implemented battery-efficient background location tracking with Kalman filter smoothing to reduce GPS noise, and built voice-guided turn-by-turn navigation with order priority queuing.
Restaurant Dashboard
Developed real-time order management portal with live rider tracking on interactive map, created automated order assignment logic with manual override capability, and built analytics dashboard showing delivery performance metrics and rider utilization rates.
Optimization & Launch
Conducted A/B testing comparing new routing algorithm vs baseline (25% improvement confirmed), fine-tuned heuristic weights based on real-world feedback from pilot riders, and executed phased rollout starting with 50 riders before full fleet deployment.
Measurable Impact
Key Results
Direct business value delivered.
Impact Analysis
Avg Delivery Time
Before
42min
After
31min
Monthly Fuel Cost
Before
$6,500
After
$4,550
Orders per Rider
Before
18/day
After
24/day
Technology Stack
Tools & Frameworks
Implementation
Dijkstra Route Optimization
Custom Dijkstra implementation that finds the shortest delivery path considering real-time traffic.
1async function optimizeRoute(orders, riderLocation) {2 // Build graph with order locations as nodes3 const graph = buildLocationGraph([riderLocation, ...orders]);4 5 // Get traffic data from Google Maps6 const trafficMatrix = await getTrafficMatrix(graph.nodes);7 8 // Apply Dijkstra with traffic weights9 const distances = {};10 const previous = {};11 const unvisited = new Set(graph.nodes);12 13 distances[riderLocation.id] = 0;14 15 while (unvisited.size > 0) {16 const current = getMinNode(unvisited, distances);17 unvisited.delete(current);18 19 for (const neighbor of graph.neighbors(current)) {20 const trafficDelay = trafficMatrix[current][neighbor];21 const distance = graph.distance(current, neighbor) + trafficDelay;22 const alt = distances[current] + distance;23 24 if (alt < (distances[neighbor] || Infinity)) {25 distances[neighbor] = alt;26 previous[neighbor] = current;27 }28 }29 }30 31 return reconstructPath(previous, orders);32}Performance
Performance Audits
"Our riders love it. Customers get food faster. Win-win."
