Research on Intelligent Solution of Service Industry Supply Chain Network Optimization Based on Genetic Algorithm

Area of Science:

• Operations Research
• Service Management
• Big Data Analytics

Background:

• The rise of big data (BD) has increased customer expectations for personalized services and customization.
• Optimizing service industry supply networks and task allocation are critical research areas.
• Understanding customer behavior and personalized demands is essential for service innovation.

Purpose of the Study:

• To optimize the service industry supply network under the big data environment.
• To study task allocation optimization considering personalized customer demand and user behavior.
• To enhance the efficiency and stability of service supply chain network optimization.

Main Methods:

• Analysis of customer personalized demand and user behavior.
• Optimization of the service industry supply chain network using a genetic algorithm (GA).
• Design of genetic operators to prevent premature convergence and improve algorithm efficiency.

Main Results:

• The improved GA demonstrates fast network optimization running times.
• Experimental results show an average running time of 54.1 seconds for m=8 and n=40.
• The algorithm exhibits high stability in optimizing service supply networks.

Conclusions:

• The proposed GA-based approach effectively optimizes service industry supply networks.
• The method addresses the need for personalized services in the big data era.
• The enhanced algorithm provides a fast and stable solution for task allocation in service supply chains.