Flexible job shop scheduling method based on improved genetic algorithm

A technology for improving genetic algorithms and flexible operations, applied in computing, instrumentation, data processing applications, etc., can solve problems such as difficulty in finding the best solution set, lack of local search ability, easy to fall into local optimum, etc., to enhance local search ability, The effect of improving robustness and improving quality

Pending Publication Date: 2019-07-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to address the lack of usability of the genetic algorithm commonly used in the existing job shop flexible scheduling process in the discrete flexible job shop scheduling problem. The traditional genetic algorithm has better global search capabilities, but lacks local search capabil...

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 improved genetic algorithm
  • Flexible job shop scheduling method based on improved genetic algorithm
  • Flexible job shop scheduling method based on improved genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] like Figure 1-9 shown.

[0043] A flexible job shop scheduling method based on improved genetic algorithm, which combines Powell search method to improve genetic algorithm to realize flexible job shop scheduling. The algorithm flow is as follows: figure 1 shown. here, with figure 2 Take the example of flexible shop scheduling shown as an example.

[0044]Step 1: Setting parameters: population size N=200, crossover rate α=0.85, mutation rate β=0.1, Rowell tolerance ε=0.1.

[0045] Step 2: Initialize the population, that is, generate 200 individuals of the first generation population. Initialization has a great influence on the speed and quality of the algorithm solution. The initialization scheme of this method includes: global selection, local selection and random selection. Among them, the ratio of the three initialization methods is gen...

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 an improved genetic algorithm. The method can solve the problem that an existing genetic algorithm is insufficient in usability in a discrete flexible job shop scheduling problem. The traditional genetic algorithm has a good global search capability, but the local search capability is insufficient, the premature convergence iseasy, the optimal solution set is difficult to find, the Powell search has a strong local search capability, but the defect that the Powell search is liable to be trapped in the local optimization exists. According to the genetic algorithm scheme combined with the Powell search method, the excellent global search capability of the genetic algorithm can be fully utilized, meanwhile, the local search capability of the whole algorithm is enhanced through the Powell search method, early maturing of the algorithm is avoided, and the quality of a scheduling scheme is improved. In consideration of the particularity of a chromosome coding scheme of a flexible job shop scheduling genetic algorithm, a traditional Powell search method is improved to avoid generation of an infeasible solution, so thatthe robustness and the search efficiency of the algorithm are improved.

Description

technical field [0001] The invention relates to a production height technology, in particular to a genetic algorithm-based flexible job-shop scheduling method, in particular to a flexible job-shop scheduling method based on an improved genetic algorithm combined with a Powell search method. Background technique [0002] As the state strengthens policy support and guidance for the intelligent transformation of manufacturing enterprises, manufacturing enterprises will generally implement advanced information systems to complete the digital and intelligent transformation of enterprises, so as to adapt to more intense competition in the future, so as to better adapt to Changes in the market. The Flexible Job-shop Scheduling Problem (FJSP) is an extension of the traditional Job-shop scheduling problem (JSP). In JSP, the processing equipment and processing time of all parts processes are fixed values. But in FJSP, there is not necessarily a one-to-one relationship between the pr...

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
IPC IPC(8): G06Q10/06
CPCG06Q10/06312
Inventor 杨振泰黎向锋张立果左敦稳张丽萍毕高杰李堃谢昌刚唐浩
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap