RouteMate Optimizer

[SG
by Shruti Gaikwad](/content/u/9WJNlTn3ntMLcBa2/index.html)

RouteMate Optimizer is a full-stack web app that helps food delivery riders maximize earnings and minimize travel distance by selecting the best set and sequence of orders using Dijkstra's and Knapsack algorithms—delivering a clear, mobile-friendly UI to optimize every trip within carrying and distance limits, all with transparent, manually implemented logic ideal for showcase and real-world simulation.

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Delivery Optimization

Enter your delivery constraints to optimize your route and maximize earnings

Maximum Carrying Capacity (kg)

Total weight you can carry in a single trip

Maximum Travel Distance (km)

Maximum distance you're willing to travel for deliveries

Optimize My Delivery

Available Orders

8 Total

  • Rohini Order ₹853.5kg pending
  • Dwarka Order ₹954.2kg pending
  • Karol Bagh Order ₹652.1kg pending
  • Lajpat Nagar Order ₹752.8kg pending
  • Rajouri Garden Order ₹803kg pending
  • Janakpuri Order ₹903.8kg pending
  • Saket Order ₹1004.5kg pending
  • Nehru Place Order ₹702.5kg pending

Ready to Optimize

Enter your delivery capacity and distance constraints, then click "Optimize My Delivery" to get the best route and order selection.

🔍 Dijkstra's Algorithm

Calculates shortest paths from restaurant to all customer locations using graph-based route optimization. Time complexity: O((V + E) log V)

🎒 0/1 Knapsack

Selects optimal order combination that maximizes profit within capacity constraints using dynamic programming. Time complexity: O(n × W)

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The AI prompt behind this app

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PromptCopy

Build a full-stack web application called “Delivery Partner Route & Order Optimization System”.

Goal: Assist a food delivery rider in selecting the best set of orders and delivery route to maximize earnings while minimizing travel distance and respecting carrying capacity constraints.

🛵 Delivery Boy Use Case

A delivery partner starts from a restaurant with multiple available food orders.
The system should guide the rider by recommending:

- Which orders to pick
- In what sequence to deliver them
- How to maximize profit within distance and capacity limits

🔧 Functional Requirements
1️⃣ Route Optimization (Dijkstra)

Model the city as a weighted graph:
- Nodes = restaurant + customer delivery locations
- Edges = road distances

Implement Dijkstra’s algorithm to compute the shortest paths from the restaurant to all customers.

Allow the rider to:
- Select current location (restaurant)
- Set maximum travel distance for a delivery trip

2️⃣ Order Selection (0/1 Knapsack)

Each available order should include:
- Delivery profit (₹)
- Package weight / capacity usage

Implement 0/1 Knapsack (Dynamic Programming) to select the optimal subset of orders that:
- Maximizes total profit
- Does not exceed rider’s carrying capacity

Combine knapsack output with shortest path distances to exclude infeasible orders.

3️⃣ Rider Input Interface

Create a mobile-friendly UI where the delivery partner can:
- View all available orders
- Enter maximum carrying capacity
- Enter maximum distance/time willing to travel
- Click “Optimize My Delivery”

4️⃣ Output for Delivery Partner

Display:
- Recommended orders to pick
- Optimized delivery sequence
- Total distance to be traveled
- Estimated total earnings
- Orders rejected due to distance or capacity limits

5️⃣ Backend Implementation

Implement Dijkstra’s shortest path algorithm manually.
Implement 0/1 Knapsack algorithm manually using dynamic programming.
Clearly comment code explaining algorithm steps and complexity.
No machine learning or external optimization libraries.

6️⃣ Data Storage (Optional)

Store rider preferences, orders, and locations in a database.

Allow repeated optimization runs.

🚫 Constraints

Do NOT use third-party optimization libraries.
Keep logic deterministic and explainable.

🎯 Final Output

A deployed delivery partner optimization app.

Realistic food delivery simulation.

Suitable for TY / final-year engineering submission and interviews.

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