Multi-agent Co-evolutionary Scheduling Approach Based on Genetic Reinforcement Learning

The paper presents an adaptive iterative distributed scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. The manufacturing system is scheduled by the multi-agent system where every machine and job is associated with its own software agent. Each agent learns how to select presumably good schedules, by this way the size of the search space can be reduced. In order to get adaptive behavior, genetic algorithm is incorporated to drive parallel search and the evolution direction. Meanwhile, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. The paper suggests a cooperation technique for the agents, as well. We also analyze the time and the solution and present some experimental results.