Smart factory dynamic collaborative scheduling method based on static scheduling prediction

A collaborative scheduling and static scheduling technology, applied in forecasting, genetic models, genetic laws, etc., can solve the problems of limiting the application of multi-agent methods, unable to summarize historical experience, unable to predict environmental changes, etc., to improve vision and overall concept, The effect of agile real-time scheduling and optimal global performance

Active Publication Date: 2018-06-29
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS +1
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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 w

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  • Smart factory dynamic collaborative scheduling method based on static scheduling prediction
  • Smart factory dynamic collaborative scheduling method based on static scheduling prediction
  • Smart factory dynamic collaborative scheduling method based on static scheduling prediction

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Embodiment Construction

[0026] To facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the associated drawings. Preferred embodiments of the invention are shown in the accompanying drawings. However, the present invention can be embodied in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terminology used herein in the description of the present invention is only for the purpose of describing specific embodiments, and is not intended to limit the present invention. As used herein, the term "and / or" includes any and all combinations of one or mor...

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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...

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Application Information

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IPC IPC(8): G06Q10/06G06Q10/04G06Q10/08G06Q30/08G06N3/12
CPCG06N3/126G06Q10/04G06Q10/0631G06Q10/0633G06Q10/087G06Q30/08
Inventor 朱海华王盈聪唐敦兵陈鸣郑杜张区委李习军
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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