Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Logistics distribution optimization method based on genetic-simulated annealing combination algorithm

A combined algorithm and logistics distribution technology, applied in the field of logistics intelligent distribution, can solve the problems of high labor cost and poor timeliness, and achieve the effects of reasonable planning, performance improvement and mileage saving

Active Publication Date: 2021-01-29
CHONGQING UNIV OF TECH
View PDF4 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the purpose of the present invention is to provide a method for optimizing logistics distribution based on the genetic-simulated annealing combination algorithm, which is used to solve the technical problems of poor timeliness and high labor costs in the logistics distribution process

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
  • Logistics distribution optimization method based on genetic-simulated annealing combination algorithm
  • Logistics distribution optimization method based on genetic-simulated annealing combination algorithm
  • Logistics distribution optimization method based on genetic-simulated annealing combination algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0054] Such as figure 1 As shown, an optimization method for logistics distribution based on genetic-simulated annealing combination algorithm, including:

[0055] Step 1, obtain the coordinate data of individual customers and distribution centers, and encode the coordinate data to obtain the initial population.

[0056] Step 2, establish a mathematical model, the objective function Z of the mathematical model i for:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] Among them, the character O in the above formula represents the distribution center, n represents the number of customers, m represents the number of vehicles, D k The maximum load capacity of the kth car, DM k Indicates the maximum mileage of the kth vehicle, veh indicates the number of vehicles ac...

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 a logistics distribution optimization method based on a genetic -simulated annealing combination algorithm, relates to the technical field of logistics intelligent distribution,and aims to obtain a globally optimal solution by using an improved genetic algorithm, and use the globally optimal solution as an initial solution of the improved simulated annealing algorithm to finally search the optimal solution. According to the combined algorithm mode, the defects of long optimization process and low convergence speed of the simulated annealing algorithm are overcome by utilizing the relatively high global search capability of the genetic algorithm, and the probability of convergence to a locally optimal solution can be greatly reduced by utilizing the relatively high local search capability of the simulated annealing algorithm, so that the performance of the overall algorithm is improved. According to the invention, the technical problems of poor timeliness and toohigh labor cost in the traditional logistics distribution process are solved. The service quality and the vehicle full load rate are improved, the transportation and distribution cost is reduced, thetransportation mileage is saved, and the path benefit is maximized.

Description

technical field [0001] The invention relates to the technical field of logistics intelligent distribution, in particular to an optimization method for logistics distribution based on a genetic-simulated annealing combination algorithm. Background technique [0002] With the development trend of global economic integration, the huge potential of e-commerce has been tapped, and the logistics industry has become an important embodiment of international transactions. In the process of logistics distribution, the rational planning of distribution vehicles and distribution routes is very important. Reasonable planning can greatly save vehicle consumption, economic costs and human resources. [0003] Compared with the traditional mode of manual calculation of logistics costs and paths, modern logistics services based on information technology can better meet the needs of modern business development due to its openness, globality, low cost and high efficiency. . Distribution is a ...

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): G06Q10/04G06Q10/08G06N3/12
CPCG06Q10/047G06Q10/08355G06N3/126Y02T10/40
Inventor 胡顺仁彭澍周强
Owner CHONGQING UNIV OF TECH
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
Eureka Blog
Learn More
PatSnap group products