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Class genetic method for logistic optmum

A logistics and scheme technology, applied in the genetic-like field for logistics optimization, can solve problems such as losing advantages and being unable to determine the vertices to be visited

Inactive Publication Date: 2007-10-10
JILIN UNIV
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Problems solved by technology

However, since the number of vertices in each group in GTSP is generally greater than 1, according to the original idea, GA based on conventional chromosomes can determine the order of visits of each group, but cannot determine the visited vertices of each group, so the existing GA algorithm Lost its original advantage when solving GTSP, GA is temporarily in trouble in solving GTSP

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  • Class genetic method for logistic optmum
  • Class genetic method for logistic optmum
  • Class genetic method for logistic optmum

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Embodiment Construction

[0019] 1. Related definitions

[0020] Definition 1: Take v 1 , V 2 ,..., v n The vertices in the graph G=(V, E, W) represented by the way are collectively called the original vertices.

[0021] Definition 2: If|V j |>1(j∈N(m)), then group V j It is a super vertex.

[0022] Definition 3: If v i ∈V j And |V j |=1(j∈{1, 2,..., m}), then the vertex v i (i ∈ {1, 2,..., n}) is the scattered point.

[0023] Definition 4: If the original vertex belongs to a certain super vertex, it is said to be an element of the super vertex.

[0024] Definition 5: Super vertices and scattered points are collectively called generalized vertices.

[0025] Definition 6: The Hamilton circuit containing all generalized vertices is called the GTSP circuit.

[0026] Definition 7: If the GTSP loop satisfies each original vertex to be visited at most once, it is called a legal GTSP loop.

[0027] Definition 8: If the Hamilton circuit containing all generalized vertices is not a legal GTSP circuit, it is called ...

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Abstract

The present invention discloses a quasi-genetic method for material flow optimization. Said method provides a chromosome model formed from two portions of head portion and truncus portion-chromosomoid, in which the gene of said head portion can be used for defining accessed top point of every group in current loop, and the gene of truncus portion can be used for defining a GTSP loop, so that can effective GTSP loop can be obtained, and has no need of making additional modification. At the same time of effectively raising calculation efficiency it can retain diversity of gene segments in the population. On the basis of chromosomoid and quasi-genetic operation said invention can adopt traditional genetic algorithm process to resolve the problem of material flow optimization.

Description

Technical field [0001] The present invention relates to a kind of genetic method, in particular to a kind of genetic method for logistics optimization, in which the chromosome is composed of two parts, the head and the trunk. According to this chromosome, the corresponding crossover, mutation, and type are defined. By reversing genetic operations and fitness, it can be used to solve the generalized traveling salesman problem, and the conventional traveling salesman problem can be solved at the same time. Background technique [0002] General traveling salesman problem (TSP) and generalized traveling salesman problem (GTSP) are two types of combinatorial optimization problems with a very wide range of applications. Among them, GTSP is a more complex type of problem than TSP. The generalized traveling salesman was first proposed by Henry-Labordere, Saksena and Srivastava, etc. in the context of the balance of computer records and the order of visits to welfare institutions. GTSP ca...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12G06Q10/00G06Q10/04G06Q10/08
Inventor 梁艳春吴春国杨金辉吴春连孙亮
Owner JILIN UNIV