Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Flexible job shop scheduling method based on genetic-backbone particle swarm hybrid algorithm

A hybrid algorithm and flexible operation technology, applied in the direction of genetic law, calculation, calculation model, etc., to release production capacity, improve algorithm convergence speed and solution accuracy, and efficiently schedule solutions

Pending Publication Date: 2021-02-19
BEIJING UNIV OF TECH +1
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The invention is a novel genetic-backbone particle swarm hybrid algorithm for solving the problem of flexible workshop scheduling

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Flexible job shop scheduling method based on genetic-backbone particle swarm hybrid algorithm
  • Flexible job shop scheduling method based on genetic-backbone particle swarm hybrid algorithm
  • Flexible job shop scheduling method based on genetic-backbone particle swarm hybrid algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0044] The present invention is a flexible job shop scheduling method based on a novel genetic-backbone particle swarm algorithm, which combines the backbone particle swarm algorithm and genetic algorithm to solve the flexible job shop scheduling problem. The algorithm flow is as follows figure 1 shown. Now with figure 2 The example problem shown is illustrated.

[0045] Step 1: Enter the basic data of the problem, including the number of workpieces 4, the number of equipment 6, and the processing time of each process of each workpiece on optional equipment.

[0046] Step 2: Set algorithm parameters: population size is 100, crossover probability is 0.8, mutation probability is 0.1, and the number of iterations is 200.

[0047] Step 3: Generate an initialization population, that is, generate chromosomes of 100 initial individuals; individual chromosomes are...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a flexible job shop scheduling method based on a genetic-backbone particle swarm hybrid algorithm, which can overcome the defects of low solving precision and low convergence rate of a general genetic algorithm for a flexible job shop scheduling problem. According to the method, the backbone particle swarm algorithm is used as a mutation operator, and the directionality ofindividual variation based on the optimal current swarm and the optimal individual history is realized through an improved particle position updating formula, so that the algorithm convergence speed and the solving precision are improved; in order to ensure that the backbone particle swarm algorithm can run in a continuous domain and avoid chromosome conversion, the invention provides a real number-based chromosome expression mode, and correspondingly provides a decoding method, an initialization method, a binary system-based chromosome crossover method and a post-mutation chromosome repair method adapting to real number chromosome expression.

Description

technical field [0001] The invention relates to a flexible job shop scheduling technology, in particular to a novel hybrid algorithm flexible job shop scheduling method, in particular to a flexible job shop scheduling method that combines a hybrid algorithm of a genetic algorithm and a backbone particle swarm algorithm. Background technique [0002] As advanced information and intelligent technology are widely used in industrial fields, especially in the field of manufacturing, manufacturing efficiency and product quality have been greatly improved. The application of advanced heuristic algorithm in the job-shop scheduling problem is one of the important aspects. Through the heuristic algorithm, a relatively optimal scheduling scheme can be obtained within an acceptable time cost, greatly improving the accuracy and rationality of planning and scheduling, and further improving the utilization efficiency of equipment without modifying the equipment, so as to give full play to ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/12G06N3/00G06Q50/04
CPCG06Q10/0631G06N3/126G06N3/006G06Q50/04Y02P90/30
Inventor 刘志峰汪俊龙张彩霞郭诗瑶张路
Owner BEIJING UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com