WGUPS Routing Program

c950.mp4

Overview

The WGUPS Routing Program is a comprehensive solution for optimizing package deliveries. This project utilizes advanced algorithms and data structures to simulate efficient routing and real-time package tracking. It ensures timely deliveries while meeting all program constraints, providing a scalable and user-friendly system for logistics operations.

Features

• Optimized Routing: Uses a Greedy Algorithm for minimal travel time and efficient delivery planning.
• Efficient Data Management: Implements a Chaining Hash Table for fast and reliable storage and retrieval of package data.
• Dynamic Delivery Simulation: Tracks delivery status and metrics in real-time.
• Scalable Design: Supports increasing package loads and multiple trucks without performance degradation.
• User-Friendly Interface: Simplified navigation for checking statuses, viewing metrics, and managing deliveries.
• Cross-Platform Compatibility: Runs on macOS, Windows, and Linux environments.

Table of Contents

1. Setup
2. Usage
3. Technical Details
4. Educational Insights
5. Examples
6. Future Enhancements
7. License
8. Acknowledgments

Setup

Requirements

• Python 3.11 or higher

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/wgups-routing.git
   cd wgups-routing

2. Run the program:

   python main.py

Usage

1. Launch the program using:

   python main.py

2. Navigate the interactive menu to:
   • View real-time delivery status
   • Display package details
   • Check overall metrics
   • Manage truck assignments
3. Monitor delivery progress and log details for analysis.

Technical Details

Algorithms

• Greedy Algorithm: Iteratively selects the closest delivery point, ensuring efficiency and simplicity.
• Chaining Hash Table: Handles package data with average-case $O(1)$ operations for search, insertion, and deletion.

Key Components

• Package Class: Manages package data (ID, address, deadline, weight, status).
• Truck Class: Simulates truck behavior, including capacity and route management.
• Distance Matrix: Preloaded with delivery point distances for quick lookups.

Complexity Analysis

• Greedy Algorithm: $O(n^2)$ for nested iterations over delivery points.
• Hash Table Operations: Average-case $O(1)$; worst-case $O(n)$ for high collision scenarios.

Educational Insights

1. Greedy Algorithm for Routing

Code Snippet:

# Greedy Algorithm Implementation
for package in package_list:
    nearest_package = min(remaining_packages, key=lambda p: distance_matrix[current_location][p.address])
    route.append(nearest_package)
    current_location = nearest_package.address
    remaining_packages.remove(nearest_package)

Explanation:

• The algorithm calculates the distance from the current location to all remaining delivery points.
• Selects the nearest point and updates the route iteratively.
• Time complexity: $O(n^2)$ due to nested loops for distance comparisons.

2. Chaining Hash Table

Code Snippet:

class HashTable:
    def __init__(self, size):
        self.size = size
        self.table = [[] for _ in range(size)]

    def insert(self, key, value):
        index = hash(key) % self.size
        self.table[index].append((key, value))

    def lookup(self, key):
        index = hash(key) % self.size
        for k, v in self.table[index]:
            if k == key:
                return v
        return None

Explanation:

• Efficiently stores and retrieves package data using hash functions.
• Resolves collisions with linked lists (chaining).
• Operations: Average $O(1)$, Worst $O(n)$.

3. Truck Metrics Logging

Code Snippet:

def log_truck_metrics_with_date(truck, truck_num):
    print(f"Truck {truck_num} Total Distance: {truck.total_distance}")
    print(f"Truck {truck_num} Delivery Time: {truck.delivery_time}")

Explanation:

• Logs key metrics for each truck, such as total distance and delivery time.
• Simple $O(1)$ operation per log.

4. Console Color Class

Code Snippet:

class Colors:
    RED = "\033[0;31m"
    GREEN = "\033[0;32m"
    RESET = "\033[0m"

def print_success(message):
    print(f"{Colors.GREEN}{message}{Colors.RESET}")

Explanation:

• Provides ANSI color codes for console output, improving readability and user experience.

Examples

Delivery Simulation

Example Status Checks:

9:00 AM:
Package 1: Delivered
Package 2: In Transit

10:15 AM:
Package 1: Delivered
Package 2: Delivered

12:30 PM:
All packages delivered successfully.

Future Enhancements

• Implement Dijkstra’s Algorithm for shortest path routing.
• Add support for dynamic delivery depots and multi-hub logistics.
• Introduce a visual mapping interface for real-time route visualization.
• Modularize the codebase for easier updates and maintenance.
• Explore advanced data structures like graphs for enhanced delivery modeling.

Acknowledgments

• WGU Faculty: For their guidance and resources.
• GeeksforGeeks: For insights into algorithm optimizations.
• Python Community: For providing robust libraries and tools for development.
• Nagaraj (2023): For insights on advanced algorithms like Dijkstra’s.