Smart factory dynamic collaborative scheduling method based on static scheduling prediction

Abstract

Disclosed is a smart factory dynamic collaborative scheduling method based on static scheduling prediction. After a workpiece enters into a production system, a corresponding workpiece agent is generated; every time after the workpiece agent enters into the production system or any of a processing procedure is completed, a processing unit agent is selected by the workpiece agent to perform processing procedures, a transportation unit agent is selected by the processing unit agent to transport workpiece, and if all of the procedures are completed, the transportation unit agent is selected by awarehouse unit agent to transport products to a warehouse. By means of the method, the problem of foresight lacking and overall concept lacking of a single agent of an existing multi-agent method is overcome, the foresight and overall concept of the single agent are improved through an experience summarizing mechanism and a global environment prediction mechanism, and the overall performance of the production system is optimized.

Description

technical field [0001] The present invention relates to the technical field of workshop scheduling control, in particular to a dynamic collaborative scheduling method for smart factories based on static scheduling prediction Background technique [0002] In today's era of advocating intelligence and big data, smart factories have become a new stage in the development of modern factory informatization. Smart factory is a new form of digital factory. It uses Internet of Things technology to monitor and manage production equipment and products. As an assistant, it assists managers to master the entire process. At the same time, it can use the data it collects to adapt and learn by itself. , and finally realize self-organization and self-decision in the production process. [0003] In the smart factory environment, there are many disturbances, complex orders, production has great uncertainty and variability, and scheduling is very complicated. How to meet the real-time require...

AI Technical Summary

Problems solved by technology

While improving production flexibility and robustness, the multi-agent method also has the following defects: (1) The current negotiation method of multi-agent technology is mainly based on the bidding mechanism

This "selfishness" will lead to behaviors such as negative work or vicious competition.

(2) Under the action of the bidding mechanism, the system cannot predict future environmental changes, nor can it summarize past historical experience

Altogether, these drawbacks greatly limit the application of multi-agent methods in scheduling

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