Production-data-driven dynamic job-shop scheduling rule intelligent selection method

Abstract

The invention provides a production-data-driven dynamic job-shop scheduling rule intelligent selection method and belongs to the manufacturing enterprise job shop production planning and scheduling application field. The method mainly comprises the following steps: introducing a Multi-Pass algorithm simulation mechanism, establishing a job-shop production scheduling simulation platform, and generating production planning and scheduling sample data; screening the obtained sample data and generating a scheduling parameter set; designing BP neural network models for scheduling knowledge learningunder different scheduling targets; optimizing training of the BP neural networks through a new firefly algorithm to obtain NFA-BP models; integrating the NFA-BP models under various scheduling targets into an intelligent scheduling module, which is integrated with a job shop MES system to guide on-line scheduling; manually adjusting online production planning and scheduling deviation and updatingthe scheduling parameter set, and the intelligent scheduling module carrying out online optimization learning; and the intelligent scheduling module adapted to real workshop production status outputting optimal scheduling rules according to current job conflict decision points.

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 ...

AI Technical Summary

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

Images