Supply chain resource scheduling optimization of e-commerce enterprises in international trade based on mobile edge...

Area of Science:

• Operations Research
• Supply Chain Management
• Computer Science

Background:

• Cross-border e-commerce supply chain networks (CBESCN) face significant challenges due to geographical scope, trade barriers, and complex logistics.
• The integration of the Internet of Things (IoT) presents opportunities for optimizing these networks.

Purpose of the Study:

• To address the operational optimization challenges in CBESCN within an IoT framework.
• To develop a resource scheduling model for international e-commerce supply chains.

Main Methods:

• A genetic algorithm was employed to construct and solve the resource scheduling model.
• A mobile edge computing (MEC) optimization scheme involving partial computation offloading was implemented.
• Resource allocation and offloading ratios were dynamically adjusted based on server computing power and task requirements.

Main Results:

• The proposed optimization scheme effectively utilizes supply chain resources for computing tasks.
• Significant reductions in total task execution delay were achieved.
• Consumption of node computing resources was notably decreased.

Conclusions:

• Time delay and cost adjustment strategies enhance the overall capability of the cross-border e-commerce supply chain.
• The developed optimization scheme provides an effective solution for managing CBESCN resources and improving performance.