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

Zhenyu Yin1,2,3, Fulong Xu1,2,3, Yue Li1,2,3

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Summary
This summary is machine-generated.

This study introduces a priority-based task scheduling strategy for intelligent production lines to handle massive, fast-growing task requests. The proposed Hybrid Monarch Butterfly and Ant Colony Optimization (HMA) algorithm significantly improves task completion rates and reduces energy consumption.

Keywords:
cloud-fog computinghybrid heuristicsindustrial internet of thingsintelligent production linetask scheduling

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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.