By MLRLAI | June 2025

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Food delivery in India has gone from a luxury to a daily routine—and behind this convenience lies a complex web of real-time data, smart algorithms, and machine learning models.

Ever wondered how your food from two different restaurants arrives hot and on time, even when you’re in a busy area? That’s not luck—it’s AI at work.

In this article, we break down how Swiggy and Zomato handle multi-order deliveries using advanced AI/ML-powered logistics systems.

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🍽️ The Challenge: Multiple Orders, One Delivery Agent

Imagine this scenario:

• A delivery partner is picking up food from two nearby restaurants.
  
• One customer is 3 km north, and another is 2 km south.
  
• The food types are different—one is hot biryani, the other is cold dessert.
  
• There’s traffic, road closures, and delivery time guarantees.
  

Now multiply this by millions of such orders daily across the country.
How do they get it right? The answer lies in intelligent dispatch systems and multi-agent optimization algorithms.

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🤖 AI-Powered Order Clubbing & Routing

Both Swiggy and Zomato use Order Batching Algorithms, a form of multi-order dispatch logic that decides when and how to club two or more orders for a single agent.

Here’s how it works:

1. Real-Time Geo-Mapping
   
   • The system identifies nearby restaurants and active delivery agents using GPS.
     
   • Clustering algorithms (like K-Means or DBSCAN) help group nearby orders.
     
2. Delivery Time Prediction (ETA Estimation)
   
   • Predictive ML models analyze:
     
     • Food prep time (based on restaurant history)
       
     • Real-time traffic
       
     • Weather conditions
       
   • The goal is to guarantee delivery within promised time despite batching.
     
3. Multi-Objective Optimization
   AI models optimize across:
   
   • Delivery speed
     
   • Customer satisfaction (based on priority, past complaints)
     
   • Delivery partner effort (fuel efficiency, route length)
     
   • Freshness of the food (hot vs. cold food delivery order)
     
4. Techniques used:
   
   • Genetic Algorithms
     
   • Reinforcement Learning (RL)
     
   • Dynamic Programming
     

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🛣️ Smart Route Planning

Once orders are clubbed, AI maps the most efficient sequence of pickups and drop-offs using routing algorithms.

• TSP Variants (Traveling Salesman Problem)
  Optimized routes are calculated to minimize distance and wait times.
  
• Real-Time Rerouting
  Traffic data from Google Maps and crowd-sourced signals reroute deliveries if a faster path becomes available mid-trip.
  
• Context-Aware Logic
  The AI also considers:
  
  • Traffic density
    
  • One-way streets
    
  • Delivery-specific constraints (e.g., carry cold items last)
    

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📊 Data Collection & Continuous Learning

Every delivery feeds data back into the system:

• Order Fulfillment Data – Was it late? Did the customer complain?
  
• Restaurant Prep Patterns – Are they slow during weekends?
  
• Partner Behavior – Who cancels most? Who finishes early?
  

Using this data, Swiggy and Zomato continuously retrain their ML models, improving speed and accuracy over time.

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⚙️ Tech Stack Behind the Scenes

Both companies are known to use technologies like:

• Python & TensorFlow – For ML pipelines
  
• Kafka & Spark – For real-time streaming and big data processing
  
• Mapbox/OpenStreetMap – For route visualization
  
• Kubernetes & Docker – For scalable microservices
  
• PostgreSQL, Redis – For location data and caching
  

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🔍 Real-World Impact

Metric	Pre-AI Era	Post-AI Implementation
Avg. Delivery Time	45 mins	28–32 mins
Multi-order Delivery Success	~60%	85–90%+
Customer Satisfaction Rating	3.5/5	4.4/5
Cost per Delivery	High	15–22% lower

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🚀 What Can You Learn from This?

If you’re an aspiring ML engineer or data scientist, this case shows how:

• AI improves real-world logistics, not just virtual tasks.
  
• Even simple algorithms like clustering, when scaled, have huge impact.
  
• Continuous learning and feedback loops are the backbone of modern systems.
  
• ML isn’t just about prediction—it’s about making smart decisions under uncertainty.