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A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

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  • Zhipeng Li
  • Xiumei Wei
  • Xuesong Jiang
  • Yewen Pang

Abstract

It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.

Suggested Citation

  • Zhipeng Li & Xiumei Wei & Xuesong Jiang & Yewen Pang, 2021. "A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, January.
    • Handle: RePEc:hin:jnlmpe:1796296
    DOI: 10.1155/2021/1796296
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