Job shop real-time scheduling method based on PCA-XGBoost-IRF

A technology for real-time scheduling and job workshops, applied in computing models, resources, instruments, etc., can solve problems such as high computing time costs, low operability, and insufficient real-time response to workshop disturbances, and achieve fast computing speed and complex time Low degree, improve the effect of anti-interference ability

Active Publication Date: 2021-08-13
XINJIANG UNIVERSITY
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is: In order to overcome the problems of low practical operability, high calculation time cost and insufficient real-time response ability to workshop disturbances in the tr

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  • Job shop real-time scheduling method based on PCA-XGBoost-IRF
  • Job shop real-time scheduling method based on PCA-XGBoost-IRF
  • Job shop real-time scheduling method based on PCA-XGBoost-IRF

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Embodiment Construction

[0133] Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0134]The specific flow chart of the embodiment of the real-time scheduling method for job shop based on PCA-XGBoost-IRF proposed by the present invention is as follows figure 1 shown, including the following steps:

[0135] S1: Normative Data Sample Construction

[0136] Taking a machining workshop as an example, the real-time scheduling verification of the job workshop is carried out in an uncertain environment. Managers can obtain data pairs composed of production system status and scheduling rules corresponding to different scheduling decision moments from the information system and the execution records of the server-side scheduling rule base, and form Canonical Sample Data (CSD) for scheduling knowledge mining. . That is, CSD={A1,A2,A3,...,A64,Rule}. The scheduling rule base includes 10 rules, and the detailed description of the schedu...

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Abstract

The invention discloses a job shop real-time scheduling method based on PCA-XGBoost-IRF. The method comprises the steps of 1, constructing a standard data sample; 2, pre-processing the sample data, performing abnormal value processing, class imbalance processing and normalization processing on the sample data, and segmenting a data set to meet the input requirements for decision model construction; 3, carrying out feature engineering processing on a training set, wherein the feature engineering processing comprises feature extraction, feature importance calculation and feature selection; 4, carrying out decision model construction based on an improved random forest, including random forest model construction, improvement of an RF model to obtain an IRF model, and optimization of hyper-parameters of the IRF model based on grid search; 5, performing PCA-XGBoost-IRF decision model training based on the optimal parameters; and 6, realizing the real-time selection and decision-making of a dynamic job shop scheduling rule by using a decision-making model based on PCA-XGBoost-IRF. According to the present invention, the real-time scheduling method which is more reliable and higher in robustness and generalization is provided for the intelligent scheduling research based on data driving.

Description

technical field [0001] The invention relates to the technical field of machine learning and job shop scheduling, in particular to a PCA-XGBoost-IRF-based real-time job shop scheduling method. Background technique [0002] With the rapid development of artificial intelligence, industrial Internet, and computer information technology, a new wave of industrial revolution is sweeping under the background of intelligent manufacturing. Job-shop Scheduling Problem (JSP) is a typical NP-hard problem with strong engineering application background. As an interdisciplinary research field, since Johnson established the first mathematical model for scheduling two machines in 1954, it has received extensive attention from experts in multiple interdisciplinary fields such as computer and operations research. As the direct executor of manufacturing, the workshop carries a large number of production tasks and is also a meeting place for a large amount of real-time information. As the actua...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04G06K9/62G06N20/00
CPCG06Q10/0631G06Q50/04G06N20/00G06F18/2135G06F18/241G06F18/214Y02P90/30
Inventor 袁逸萍熊攀阿地兰木·斯塔洪任年鲁
Owner XINJIANG UNIVERSITY
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