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A method for intelligent selection of dynamic job shop scheduling rules driven by production data

A job shop, production data technology, applied in the field of intelligent selection of dynamic job shop scheduling rules

Active Publication Date: 2021-07-06
CHONGQING UNIV OF POSTS & TELECOMM
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  • Description
  • Claims
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Problems solved by technology

[0005] In view of the deficiencies and deficiencies in the existing technical methods for solving job shop scheduling and scheduling in the above-mentioned background technology, the purpose of the present invention is to provide a method for intelligent selection of dynamic job shop scheduling rules driven by production data, and a production process scheduling method driven by production data First, use the existing historical scheduling information to obtain scheduling knowledge in various scheduling environments, and then build a scheduling decision-making module based on production data, which can quickly respond to new scheduling environments and generate new scheduling solutions

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  • A method for intelligent selection of dynamic job shop scheduling rules driven by production data
  • A method for intelligent selection of dynamic job shop scheduling rules driven by production data
  • A method for intelligent selection of dynamic job shop scheduling rules driven by production data

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Embodiment

[0088]The first stage is the acquisition of sample data. The simulation model of job shop scheduling takes the classic ft10 (MT10) problem as an example. Its scale is 10*10, that is, it includes 10 kinds of workpieces, 10 processing equipment, and has definite workpiece processing time information and processing path information. The establishment of a job shop production scheduling simulation platform based on Multi-Pass simulation technology can be realized through, for example, Siemens' professional production system simulation software Siemens Tecnomatix Plant Simulation 11TR3. Multiple modules such as control are input as control parameters, and software modules such as Simtalk language are used to realize various scheduling rules. The simulation platform is divided into five parts: job shop scheduling model, model initialization and order information management, scheduling rule implementation, workpiece flow control and Experimental control and output, the main function ...

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Abstract

The invention provides an intelligent selection method for dynamic job shop scheduling rules driven by production data, which belongs to the application field of job shop scheduling and scheduling in manufacturing enterprises, and mainly includes: introducing a Multi-Pass algorithm simulation mechanism, establishing a job shop production scheduling simulation platform, Generate sample data for production scheduling; screen the obtained sample data to generate a scheduling parameter set; design a BP neural network model for scheduling knowledge learning under different scheduling objectives; propose an improved firefly algorithm to optimize the BP neural network model Training to obtain the NFA-BP model; integrate the NFA-BP models under each scheduling target into an intelligent scheduling module, and integrate it with the MES system of the job shop to guide online scheduling; manually adjust the deviation of online scheduling and update scheduling parameters The intelligent scheduling module conducts online optimization learning; the intelligent scheduling module adapted to the real workshop production conditions outputs the optimal scheduling rules according to the current job conflict decision point.

Description

technical field [0001] The present invention relates to the application field of manufacturing enterprise job shop production scheduling technology, and more specifically, relates to an intelligent selection method for dynamic job shop scheduling rules driven by production data. Background technique [0002] The Job Shop Scheduling Problem (JSP) is the most important production scheduling problem, which has the characteristics of multi-objective, dynamic randomness, and computational complexity, and has been proved to be NP-hard. After decades of development, researchers have proposed many algorithms for solving job shop scheduling problems, including scheduling methods based on operations research theories such as branch and bound and mathematical programming, scheduling methods based on scheduling rules, and scheduling methods based on bottlenecks. Method, a scheduling method based on intelligent computing theories such as artificial neural network, genetic algorithm, and ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06N3/08G06N3/12G06Q10/04
Inventor 罗蓉刘磊尹胜罗志勇沈勋耿琦琦
Owner CHONGQING UNIV OF POSTS & TELECOMM
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