Improved solving algorithm for flexible flow shop scheduling problem

A flexible flow shop and scheduling problem technology, applied in computing, instruments, data processing applications, etc., to achieve the effects of enhancing global search capabilities, avoiding premature convergence, and shortening time

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
View PDF0 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] 2) Each workpiece cannot be interrupted once it starts processing;
[0007] 3) Each machine M j Only one workpie...

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
  • Improved solving algorithm for flexible flow shop scheduling problem
  • Improved solving algorithm for flexible flow shop scheduling problem
  • Improved solving algorithm for flexible flow shop scheduling problem

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] Step 1: FSSP algorithm definition and process, its specific description is as follows:

[0025] Step 1.1) FSSP algorithm definition

[0026] 1) Substitution sequence: the position of the particle is X, and the operation bit of the replacement sequence (i, j) exchanges the i-th element and the j-th element in X.

[0027] 2) Addition operator Add the permutation sequence of the latter velocity to the end of the permutation sequence list of the previous velocity or position. For example: suppose So

[0028] 3) Subtraction operator The subtraction operation is the global optimal solution of the particle minus the particle's individual position or the particle's individual optimal solution position minus the particle's individual position, and the result is a permutation sequence. For example: suppose A=(3,1,2,4,5,6), B=(1,2,4,3,6,5), because A(1)=B(4)=3, the first The exchange sequence is (1,4), that is to say, the first position of the subtraction result is 4,...

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 provides an improved solving algorithm for a flexible flow shop scheduling problem. Particle velocity and position correlation operators are redefined. A coding matrix and a decoding matrix are introduced to represent the relationship among workpiece, machine and scheduling. In order to improve the initial group quality of the improved discrete particle swarm algorithm for the flexible flow shop scheduling problem, a shortest time decomposition policy algorithm based on an NEH algorithm is proposed for the first time by analyzing the relationship between initial machine selection and total completion time of scheduling. The improved discrete particle swarm algorithm is used for global optimization. The algorithm has the advantages that the quality of the initial solution of a particle population is improved; and the time for particles to find the optimal solution is shortened; and an inertial weighting method with exponential decrease is used to effectively avoid the premature convergence of the particles and enhance the early global search ability of the particles.

Description

technical field [0001] The invention relates to the field of job shop scheduling. Background technique [0002] Flexible Flow Shop Scheduling Problem (FFSP) is the most commonly used simplified model for a large number of actual production line scheduling problems. It is an important class of combinatorial optimization problems and has become the key to the practice of advanced manufacturing technology. FFSP has been proven to be an NP-hard problem, and it is a subject of common concern in both academia and engineering. Therefore, the research on FFSP has important theoretical significance and application value. [0003] At present, the main methods for solving FFSP include genetic algorithm, particle swarm optimization algorithm, immune clone selection algorithm#ant colony algorithm, etc., there is a method to use the stereotype heuristic algorithm to generate the initial population, and combine the genetic algorithm for global search; Solve the flexible flow shop schedul...

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
CPCG06Q10/0631
Inventor 范勇胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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