Method for optimizing logistics distribution center site selection by applying improved hybrid immune algorithm

A hybrid immune and logistics distribution technology, applied in logistics, data processing applications, computing, etc., can solve the problems of not considering the transportation cost from the factory to the distribution center, and the immune algorithm cannot quickly converge to the global optimal problem, so as to avoid falling into the local optimal, avoid falling into local optimum, and improve the effect of antibody diversity

Pending Publication Date: 2020-06-30
NEIJIANG NORMAL UNIV
View PDF12 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] (3) Does not consider the transportation costs from the factory to the distribution center
[0033] In summary, th

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
  • Method for optimizing logistics distribution center site selection by applying improved hybrid immune algorithm
  • Method for optimizing logistics distribution center site selection by applying improved hybrid immune algorithm
  • Method for optimizing logistics distribution center site selection by applying improved hybrid immune algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0084] The present invention will be described in further detail below in conjunction with specific embodiments.

[0085] A method for optimizing the site selection of a logistics distribution center by applying an improved hybrid immune algorithm, specifically including the following steps:

[0086] (1) Establish a model for logistics center location selection

[0087] The logistics center mentioned meets the following conditions:

[0088] 1) The scale and capacity of the distribution center can always meet the needs of the demand point, and is determined by the demand within its distribution radiation range;

[0089] 2) One demand point is only supplied by one distribution center;

[0090] 3) The shipping cost from factory to distribution center is not considered.

[0091] The objective function is established as:

[0092]

[0093] The constraints are:

[0094]

[0095] Z ij ≤ h j ,i∈N,j∈M i (3)

[0096] in:

[0097] Among them, N={1,2,3,L,n} is the sequen...

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 method for optimizing logistics distribution center site selection by applying an improved hybrid immune algorithm. The method comprises the following steps: (1) establishinga logistics center site selection model; (2) performing immune algorithm calculation; and (3) carrying out improved hybrid immune algorithm calculation. The method has the beneficial effects that aiming at the problem that a conventional immune algorithm is easy to fall into local optimum, simulated annealing is used in an immune link to realize dynamic threshold selection so as to modify a function expected value in real time, and meanwhile, random single-point crossover operation, high-frequency variation and other operations are adopted in immunization to ensure the diversity of antibodies. The improved algorithm improves antibody diversity so as to avoid falling into a local optimal value and accelerate convergence speed. Dynamic threshold selection, random single-point crossover operation, high-frequency variation and other operations are carried out by using function propagation expectation of a simulated annealing correction immune clone algorithm to improve population diversity so as to avoid falling into local optimum, time complexity is reduced, and convergence speed is also accelerated.

Description

technical field [0001] The invention belongs to a logistics distribution method, in particular to a method for optimizing the site selection of a logistics distribution center by applying an improved hybrid immune algorithm. Background technique [0002] The existing logistics center location problem is mainly based on the following assumptions: [0003] (1) The scale and capacity of the distribution center can always meet the needs of the demand point, and is determined by the demand within its distribution radiation range; [0004] (2) A demand point is supplied by only one distribution center; [0005] (3) The transportation cost from the factory to the distribution center is not considered. [0006] The objective function is: [0007] [0008] The constraints are: [0009] [0010] Z ij ≤ h j ,i∈N,j∈M i (3) [0011] in: [0012] Among them, N={1,2,3,L,n}, is the sequence number set of all demand points; M i is the set of alternative distribution center...

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): G06Q10/08G06Q30/02G06N3/00
CPCG06Q10/0836G06Q30/0205G06N3/006
Inventor 陈超武菊刘芳
Owner NEIJIANG NORMAL 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