Logistics Optimization Strategy Based on Deep Neural Framework

Area of Science:

• Artificial Intelligence
• Operations Research
• Computer Science

Background:

• E-commerce peak periods suffer from low product delivery rates and delays.
• Current logistics optimization methods struggle with dynamic demand and complex networks.

Purpose of the Study:

• To develop an advanced logistics optimization method using improved graph convolutional networks.
• To enhance product delivery rates and timeliness during e-commerce peak periods.
• To increase the return on investment for logistics operations.

Main Methods:

• Incorporated a tensor rotation module into graph convolution layers for global feature extraction.
• Integrated inception structures into temporal convolution layers for multiscale temporal analysis and reduced computational load.
• Utilized e-commerce platform logistics data for experimental validation.

Main Results:

• Significantly accelerated logistics planning speed.
• Improved product delivery rates and ensured timely product delivery.
• Demonstrated a substantial increase in return on investment.

Conclusions:

• The proposed improved graph convolutional network method effectively optimizes e-commerce logistics.
• The method addresses key challenges during peak periods, enhancing efficiency and profitability.
• This approach offers a scalable solution for real-world logistics optimization.