WGUPS Package Delivery Simulation

Overview

This project simulates a real-time package delivery system for Western Governors University Parcel Service (WGUPS) using the Nearest Neighbor Algorithm. It was developed as part of the C950: Data Structures and Algorithms II course at Western Governors University.

The program dynamically manages package deliveries across multiple trucks and drivers, accounting for constraints such as delayed packages and wrong addresses, all while minimizing mileage. A GUI interface and a headless mode are provided to visualize or simulate the process.

---

Features

• 🚚 Multiple Truck Management with dynamic driver assignment
• 🬝 Greedy Nearest Neighbor Algorithm for route optimization
• 🕒 Real-time Clock Simulation starting at 8:00 AM
• 💃 Custom Hash Table with resizing for package lookups (O(1) time complexity)
• 📦 Support for Delayed Packages and incorrect addresses
• 🖥️ Interactive GUI built with tkinter
• 🔄 Step-by-step simulation controls (manual or automatic speed)
• 🧪 Headless mode for command-line testing

---

File Structure

.
├── main.py               # GUI application for live simulation
├── headless.py           # Console-based simulation for quick testing
├── Model/
│   ├── Dispatcher.py     # Controls trucks, drivers, and dispatch logic
│   ├── Truck.py          # Truck behavior and movement logic
│   ├── Driver.py         # Driver data model
│   ├── Package.py        # Package object model
│   ├── HashTable.py      # Custom hash table implementation
│   ├── Utils.py          # CSV loading utilities for distances and packages
├── Data/
│   ├── distances.csv     # Symmetric distance matrix
│   ├── packages.csv      # Package list with delivery deadlines and special notes
├── Task 2 Writing.pdf    # Design and algorithm justification (WGU submission)

---

How It Works

• Packages are loaded into a Hash Table from packages.csv.
• Trucks are initialized with pre-assigned packages.
• Each truck uses the Nearest Neighbor Algorithm to determine its next delivery.
• Special delivery constraints (delays, wrong addresses) are automatically handled.
• Drivers can dynamically switch trucks to optimize delivery timing.

---

Requirements

• Python 3.8+
• tkinter (pre-installed with most Python distributions)

---

Running the Simulation

Option 1: GUI Mode

Launch the interactive delivery simulator:

python main.py

Features:

• Pause, resume, and step-through simulation
• Visual tracking of each truck’s location, miles traveled, and packages delivered
• Color-coded statuses (Queued, Delivered, Delayed)

Option 2: Headless Mode

Run the simulation in the terminal:

python headless.py

Displays live truck activity and prints all package statuses until delivery is complete.

---

Algorithm Justification

This project uses a greedy nearest neighbor algorithm due to its simplicity and efficiency. It was chosen over alternatives like Genetic Algorithms or Simulated Annealing due to time constraints and project scope. Full justification and comparison of algorithms are included in Task 2 Writing.pdf.

---

Potential Improvements

• 🔀 Dynamic clustering of delivery addresses (e.g., K-Medoids) to reduce mileage
• 📊 Advanced analytics dashboard (total mileage, on-time percentage)
• 🧠 Implementing smarter truck-to-package assignment for future scalability

---

Author

Rafael Estrella
WGU Student ID: 009970725
Course: C950 – Data Structures and Algorithms II
Instructor: Dr. Amy Antonucci

---