A single job shop scheduling method for multi-agent deep reinforcement learning

A job shop, reinforcement learning technology, applied in the field of shop scheduling

Active Publication Date: 2021-08-27
DONGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

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

Method used

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  • A single job shop scheduling method for multi-agent deep reinforcement learning
  • A single job shop scheduling method for multi-agent deep reinforcement learning
  • A single job shop scheduling method for multi-agent deep reinforcement learning

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[0048] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0049] A single-piece job shop scheduling method for multi-Agent deep reinforcement learning provided by the present invention comprises the following steps:

[0050] Step 1. Use the multi-agent method to carry out distributed modeling on the single job shop scheduling environment.

[0051] Such as figure 1 Shown, be the multi-Agent reinforcement learning model of the present invention, comprise the following content:

[005...

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Abstract

Aiming at the characteristics of complex constraints and many types of solution spaces for single job shop scheduling problems, the present invention proposes a single job shop scheduling problem based on multi-agent deep reinforcement learning. Job shop scheduling method. The present invention firstly designs the communication mechanism among multiple agents, and adopts the multi-agent method to model the reinforcement learning of the scheduling problem of a single job workshop; secondly, constructs a deep neural network to extract the status of the workshop, and designs the action selection mechanism of the job workshop on this basis , to achieve the interaction between the workshop processing workpiece and the workshop environment; again, design a reward function to evaluate the entire scheduling decision, and use the PolicyGradient algorithm to update the scheduling decision to obtain better scheduling results; finally use the standard data set to The performance of the algorithm is evaluated and verified. The invention can solve the job shop scheduling problem, and enriches the method system of the job shop scheduling problem.

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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06N3/08G06N3/04
CPCG06Q10/04G06Q10/0631G06Q10/067G06Q50/04G06N3/08G06N3/048Y02P90/30
Inventor 张洁赵树煊汪俊亮贺俊杰
Owner DONGHUA UNIV
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