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Projects/Restaurant Delivery SuiteFood & Logistics

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

Managing a fleet of 200+ delivery riders across multiple restaurant partners required precise geofencing, dynamic route optimization as orders came in, and battery-efficient location tracking that wouldn't drain rider phones.

Our Approach

We integrated Google Maps Directions API with a custom Dijkstra-based algorithm that considers real-time traffic, order priorities, and rider availability to calculate optimal routes.
Phase 1
Drone Delivery Integration
Phase 2
AI Predictive Demand Analysis
Phase 3
Autonomous Vehicle Support
Phase 4
Voice-guided Navigation

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

Location Updates: rider β†’ socket
Route Request: socket β†’ router
Get Directions: router β†’ maps
Save Route: router β†’ mongo

Development Journey

From Concept to Launch

2 weeks

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.

6 weeks

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.

8 weeks

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.

4 weeks

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.

4 weeks

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

Primary Outcome
25% Faster Deliveries

Direct business value delivered.

-25%
Delivery Time
Average reduction
-30%
Fuel Costs
Monthly savings
200+
Active Riders
Managed simultaneously

Impact Analysis

Avg Delivery Time

Before

42min

After

31min

26% Faster

Monthly Fuel Cost

Before

$6,500

After

$4,550

30% Savings

Orders per Rider

Before

18/day

After

24/day

33% More Efficient

Technology Stack

Tools & Frameworks

React NativeNode.jsGoogle MapsSocket.ioMongoDBRedisAWS ECSFirebase

Implementation

Dijkstra Route Optimization

Custom Dijkstra implementation that finds the shortest delivery path considering real-time traffic.

logic.js
1async function optimizeRoute(orders, riderLocation) {
2 // Build graph with order locations as nodes
3 const graph = buildLocationGraph([riderLocation, ...orders]);
4
5 // Get traffic data from Google Maps
6 const trafficMatrix = await getTrafficMatrix(graph.nodes);
7
8 // Apply Dijkstra with traffic weights
9 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

94
Performance
88
SEO
91
Accessibility
97
Best Practices
"Our riders love it. Customers get food faster. Win-win."
Operations Director, Food Delivery Co.
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