Single job shop scheduling method for multi-Agent deep reinforcement learning

Abstract

The invention provides a single-piece job-shop scheduling method based on multi-Agent deep reinforcement learning, aiming at the characteristics that the single-piece job-shop scheduling problem is complex in constraint and various in solution space types, and the traditional mathematical programming algorithm and meta-heuristic algorithm cannot meet the quick solution of the large-scale job-shopscheduling problem. The method comprises the following steps: firstly, designing a communication mechanism among multiple Agents, and carrying out reinforcement learning modeling on a single job shopscheduling problem by adopting a multi-Agent method; secondly, constructing a deep neural network to extract a workshop state, and designing an operation workshop action selection mechanism on the basis of the deep neural network to realize interaction between a workshop processing workpiece and a workshop environment; thirdly, designing a reward function to evaluate the whole scheduling decision,and updating the scheduling decision by using a PolicyGraphic algorithm to obtain a more excellent scheduling result; and finally, performing performance evaluation and verification on the algorithmperformance by using the standard data set. The job shop scheduling problem can be solved, and the method system of the job shop scheduling problem is enriched.

Description

technical field [0001] The invention relates to the field of workshop scheduling, and the research problem is the most common single-piece operation workshop scheduling problem in production. Background technique [0002] Manufacturing industry is the pillar industry of our country. Modern manufacturing enterprises have many production links and complex cooperative relations. Reasonable production scheduling is of great significance to improve enterprise production efficiency, reduce costs and shorten production cycle. The job-shop scheduling problem (job-shop scheduling problem, JSP) is the most common job-shop scheduling problem, which reflects the mapping relationship between allocating manufacturing tasks and resources under the constraints of shop materials and processes. Improving the production efficiency of manufacturing enterprises is of great significance and is a subject of extensive research in academia and engineering. [0003] The JSP problem is complex to sol...

AI Technical Summary

Problems solved by technology

At present, there is still a lack of research on deep reinforcement learning on JSP issues in China

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