Job-shop scheduling method with cache constraints based on improved genetic algorithm

An improved genetic algorithm and job shop technology, applied in the field of job shop scheduling with cache constraints, can solve problems such as little research

Active Publication Date: 2021-01-22
SOUTHWEST JIAOTONG UNIV
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, there has been little research on the solution method of the job shop scheduling problem with cache constraints, and the solution idea is to focus on the coarse-grain

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
  • Job-shop scheduling method with cache constraints based on improved genetic algorithm
  • Job-shop scheduling method with cache constraints based on improved genetic algorithm
  • Job-shop scheduling method with cache constraints based on improved genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0116]Examples:

[0117]The invention selects a standard job shop benchmark example to test the performance of the algorithm. Based on the standard calculation examples La01~La20, construct the percentage of the machine buffer capacity to the number of processed workpieces. For example, the scale of the calculation example La01 is 10x5, which means that the number of workpieces is 10 and the number of machines is 5; the percentage of the machine buffer capacity to the number of processed workpieces is 10. %, it means that the cache capacity of each machine is 1.

[0118](1) Experimental parameter settings

[0119]The initial population size, cross-mutation probability, and number of iterations of the improved genetic algorithm will all affect the convergence of the algorithm. In this paper, the population size is set at 50-200, and the number of iterations is 50-200. , The convergence of the algorithm is better when the number of iterations Gmax=100. The specific parameter assignments of the...

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 job-shop scheduling method with cache constraints based on an improved genetic algorithm. The method specifically comprises the steps: firstly building a job-shop schedulingmathematic model with the cache constraints; then, an improved genetic algorithm is used for optimization solution, a traditional job shop scheduling procedure and double-layer coding of a machine areadopted, and a reasonable procedure adjustment method is designed for decoding; the algorithm is improved by adopting the adaptive crossover mutation probability in combination with an improved variety crossover operator, and a solving model is optimized. According to the method, a solution with higher precision can be obtained under the same cache capacity, and the efficiency and convergence capacity of the algorithm are improved.

Description

technical field [0001] The invention belongs to the field of job shop scheduling, in particular to a job shop scheduling method with cache constraints based on an improved genetic algorithm. Background technique [0002] The job shop scheduling problem is one of the most common scheduling problems, and many scholars have conducted in-depth and extensive research in this field for a long time. In the current research, when constructing the scheduling model, the cache capacity of the machine is generally set to be infinite, and the impact of the cache capacity on job scheduling is ignored. In the actual production process, this assumption leads to the inapplicability of many job shop mathematical models. Inter-machine buffering can hold product parts after processing an equipment unit or supply the next equipment unit in an adjacent process step, so in-production buffering is critical to mitigate sudden changes in the production line. However, in the pursuit of "zero invento...

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): G06F30/20G06Q10/06G06N3/12
CPCG06F30/20G06Q10/0631G06N3/126G06Q10/06G06N5/01G06F9/5027
Inventor 张剑江磊罗焕胡明珠郑婷娟
Owner SOUTHWEST JIAOTONG UNIV
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