DAA PROJECT — LOGISTICS OPTIMIZATION

Smart Delivery
Algorithmic Engine

Implements Merge Sort, Dijkstra, Greedy Knapsack, Dynamic Programming & Divide and Conquer — all live, all real.

2-Wheeler
Weight < 25 kg
4-Wheeler Small
25 kg – 45 kg
4-Wheeler Big
Weight > 45 kg
⚙️
Module 1 — Sorting
Merge Sort · Quick Sort · Heap Sort. Prioritize deliveries by deadline, weight, or priority.
O(n log n)
Module 2 — Graph Routes
Dijkstra & Bellman-Ford for optimal city-to-city delivery paths.
O((V+E) log V)
Module 3 — Greedy
Fractional Knapsack for real-time vehicle loading maximization.
O(n log n)
Module 4 — DP Knapsack
0/1 Knapsack for globally optimal package selection per vehicle.
O(n × W)
Module 5 — Divide & Conquer
Closest pair of warehouses using recursive divide-and-conquer.
T(n) = 2T(n/2) + O(n)

📦 Delivery Prioritization

Sort incoming packages using Merge Sort (stable), Quick Sort (fast), or Heap Sort (in-place)

1 Configure Packages
2 Choose Algorithm
Merge Sort
Quick Sort
Heap Sort
Click "Generate & Sort" to see results...

🗺️ Route Optimization

Find shortest delivery path using Dijkstra or Bellman-Ford on a city graph

1 Graph Setup
Dijkstra
Bellman-Ford
2 City Network
Select cities and click Find Route...

Vehicle Loading — Fractional Knapsack

Greedily maximize delivery value within vehicle weight capacity

1 Vehicle Capacity
2 Loading Result
Generate packages and run the algorithm...

0/1 Knapsack — Optimal Selection

DP ensures globally optimal selection — greedy may fail here

1 Setup
2 DP Result
Generate packages and run DP...

Closest Warehouse Pair

Divide & Conquer closest-pair algorithm — T(n) = 2T(n/2) + O(n)

1 Warehouse Grid
2 Result
Generate warehouses and run algorithm...