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

Three-dimensional encasement novel genetic algorithm model under multi-constrain condition

A genetic algorithm and dimension packing technology, applied in the direction of gene model, etc., can solve the problems of restricting the development of the container transportation industry, less handling of packing problems, and lower transportation costs

Inactive Publication Date: 2015-06-03
SOUTHWEAT UNIV OF SCI & TECH
View PDF3 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] Due to the above defects, the development of the container transportation industry is greatly limited, which is not conducive to the reduction of transportation costs

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
  • Three-dimensional encasement novel genetic algorithm model under multi-constrain condition
  • Three-dimensional encasement novel genetic algorithm model under multi-constrain condition
  • Three-dimensional encasement novel genetic algorithm model under multi-constrain condition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] figure 1 The algorithm flow chart is used for this algorithm model. In order to further illustrate the content, effects and innovations of the present invention, the technical details will be further elaborated below.

[0026] For the bin packing problem, the chromosome structure used in this genetic algorithm model is: DNA={G 1 ,G 2 ,G 3 ,…,G M ,} ,in G i Indicates the first i Each cargo, including the input information of size, weight, center of gravity, and maximum load. M is the chromosome length, it is easy to know, M The larger it is, the slower the algorithm executes. In order to speed up the processing speed of the genetic algorithm, this model adopts preprocessing measures, that is, stacking and combining small goods into large goods. In order to ensure that the combined algorithm can quickly converge to the optimal value, the combined conditions are as follows:

[0027] 1. Each combined cargo is composed of no more than Г small cargo stacks (in...

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 relates to a three-dimensional encasement novel genetic algorithm model under a multi-constrain condition. At present, the logistics transportation industry rapidly develops, but encasement plan decision models are not perfect; particularly, a three-dimensional encasement model under the multi-constrain condition has the following typical problems: 1, the time complexity is higher; 2, the space utilization ratio is lower; 3, a encasement plan cannot be perfect. The design considers the constraint conditions such as the space utilization ratio, the centre-of-gravity position and the load bearing in the encasement problem, combines an improved genetic algorithm with a Monte Carlo method, a gene injection algorithm and a non-dominated sorting algorithm, aims at improving the space utilization ratio of the encasement plan and reducing the time complexity of the algorithms under the multi-constrain condition, and belongs to the field of intelligent arithmetic optimization. The three-dimensional encasement novel genetic algorithm model is mainly characterized in that population is initialized through the Monte Carlo method based on normal distribution; the gene injection algorithm is used in the encasement decision mode; the probability of crossover, mutation and gene injection operators is fitness functions; an online space combining method is used.

Description

technical field [0001] The present invention is mainly aimed at optimizing the container packing algorithm model under multi-constraint conditions, involving multi-objective optimization, genetic algorithm (genetic algorithm, GA), Monte Carlo (Monte Carlo, MC) method, non-dominated sorting (Non-Dominated Sorting) algorithm . The problems existing in the genetic algorithm aimed at optimizing the bin packing decision, including high time complexity, low space utilization, and insufficient bin packing scheme, belong to the field of intelligent algorithm optimization. Background technique [0002] As an advanced modern transportation method, container transportation uses containers as a transportation unit for cargo transportation. It can comprehensively utilize railways, highways, waterways and aviation for multimodal transportation to achieve door-to-door transportation, which is conducive to reducing transportation links. With the rapid growth of the national economy and the...

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): G06N3/12
Inventor 张秋云刘寅刘燕熊凯郭秋梅江虹
Owner SOUTHWEAT UNIV OF SCI & 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