High Performance Computing Simulation of Intelligent Logistics Management Based on Shortest Path Algorithm

Area of Science:

• Logistics Management
• Artificial Intelligence
• Operations Research

Background:

• E-commerce growth necessitates advancements in logistics for cost reduction and efficiency.
• Intelligent technology is crucial for meeting the increasing demands of modern logistics management.
• Current logistics challenges include optimizing transportation routes and minimizing operational expenses.

Purpose of the Study:

• To explore intelligent solutions for enhancing logistics management efficiency and cost-effectiveness.
• To identify and adapt shortest path algorithms for complex, information-rich logistics environments.
• To develop a novel algorithm that improves upon existing methods for route optimization and obstacle avoidance.

Main Methods:

• Comparative analysis of Dijkstra and A* (A-star) algorithms in the context of logistics.
• Integration of the ant colony algorithm with the A* (A-star) algorithm to create an A*-ant colony hybrid.
• Performance evaluation through simulation experiments and testing of the proposed algorithm.

Main Results:

• The A* (A-star) algorithm demonstrates greater suitability than Dijkstra for large-scale logistics data.
• The A*-ant colony algorithm combines the strengths of both parent algorithms, offering high efficiency and stability.
• The developed algorithm shows excellent optimization capabilities, leading to significant cost reductions in logistics.

Conclusions:

• The A*-ant colony algorithm is a highly effective tool for intelligent logistics management.
• This hybrid approach successfully addresses the need for reduced transportation distances and lower costs.
• The algorithm's performance validates its potential for practical application in the e-commerce logistics sector.