A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

Area of Science:

• Industrial Internet of Things (IIoT)
• Cloud-Fog Computing Architectures
• Intelligent Manufacturing Systems

Background:

• Exponential growth in task requests from smart terminals in intelligent production lines necessitates efficient task management.
• Current task scheduling methods struggle to meet the demands for rapid response and resource optimization.

Purpose of the Study:

• To address the multi-objective task scheduling problem in intelligent production lines.
• To minimize both service delay and energy consumption for time-sensitive tasks.

Main Methods:

• Establishment of a cloud-fog computing architecture tailored for intelligent production lines.
• Development of a multi-objective function for task scheduling.
• Implementation of an improved hybrid monarch butterfly and ant colony optimization algorithm (HMA).

Main Results:

• HMA demonstrated superior performance compared to other algorithms in task completion rate.
• Achieved over 90% task completion rate with more than 10 nodes, meeting real-time requirements.
• Outperformed other algorithms in terms of maximum completion rate and power consumption.

Conclusions:

• The proposed HMA algorithm effectively optimizes task scheduling in intelligent production lines.
• The strategy successfully balances minimizing service delay and energy consumption.
• HMA provides a robust solution for handling massive task loads in real-time IIoT environments.