Smart Cargo Route Planning System: A Logistics AI Solution Based on Greedy Algorithm and Heuristic Optimization

Project Core

This project is an intelligent cargo route planning system developed by İrem Nisa Sözen, a student from the Department of Software Engineering at Celal Bayar University in Manisa, Turkey. It combines classic greedy algorithm and heuristic optimization strategies to improve logistics delivery efficiency through multi-scenario simulation evaluation.

Key Information

• Source: GitHub project (link: https://github.com/irem-48/smart-cargo-route-planner)
• Core content: Compare the classic greedy algorithm with the optimized intelligent greedy strategy, and verify the latter's advantages in delivery efficiency and success rate.
• Positioning: An artificial intelligence course assignment with both practical and teaching value.

Logistics delivery route planning is a classic NP-hard problem in the fields of operations research and AI. With the development of e-commerce, cargo volume has surged, and traditional manual scheduling can hardly handle complex constraints such as package location distribution, delivery deadlines, priority differences, and traffic delays. As an AI course assignment, this project aims to solve this practical problem using algorithmic methods and demonstrate the value of heuristic approaches in combinatorial optimization.

Classic Greedy Algorithm

Select the local optimal (e.g., the nearest or most urgent delivery point) at each step. Its advantages are simple implementation and fast computation, but it easily falls into local optima and cannot consider the global path structure.

Optimized Intelligent Greedy Algorithm

Introduce heuristic improvements based on the classic greedy algorithm, considering comprehensively:

• Spatial clustering of package locations
• Urgency of delivery deadlines
• Package priority weights
• Traffic delay risk estimation This strategy is closer to the global optimal solution while maintaining efficiency.

The project provides a complete simulation evaluation framework with the following functions:

1. Random scenario generation: Generate diverse scenarios including package quantity, location, deadline, priority, etc.
2. Multi-scenario simulation: Run 50 random scenarios to evaluate the average performance of the algorithm.
3. Quantitative metrics: Evaluate performance from three aspects (efficiency, reliability, service quality) through indicators such as average path distance, number of delayed packages, overall delivery success rate, and emergency package success rate.

Technical Implementation

• Language: Pure Python, relying only on standard libraries math and random, lightweight and easy to deploy.
• Code structure: Scenario generation module, classic greedy implementation, optimized greedy implementation, performance comparison module.
• Running method: Execute python main.py to run the complete simulation process.

Educational Significance

It demonstrates the complete process from problem modeling, algorithm design to experimental evaluation, making it an ideal practical case for learning algorithms and operations research.

Value of Heuristic Optimization

• Real-time performance: Quickly respond to logistics scheduling needs.
• Dynamic adaptation: Easily handle new orders or unexpected situations.
• Interpretability: The decision-making process is easier to understand and debug than deep learning models.

Expansion Directions

• Introduce meta-heuristic methods such as genetic algorithms and simulated annealing for comparison.
• Add multi-vehicle scheduling (considering capacity constraints).
• Integrate OpenStreetMap real map data.
• Explore reinforcement learning strategies.

This project is an excellent AI course case. Through systematic simulation evaluation, it proves that the optimized intelligent greedy strategy has significant improvements in delivery success rate and efficiency compared to the classic greedy algorithm. As an open-source project, it not only provides a feasible algorithm solution for logistics route planning but also offers a clear and complete reference implementation for AI and operations research learners.