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AGV scheduling method based on ant colony and genetic algorithm

A genetic algorithm and scheduling method technology, applied in the field of AGV scheduling, can solve problems such as inability to converge, difficult scheduling, and falling into local optimum

Pending Publication Date: 2020-10-30
无锡弘宜智能科技有限公司
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AI Technical Summary

Problems solved by technology

However, when ant colony algorithm and genetic algorithm solve the practical problems of AGV (Automated Guided Vehicle) scheduling, they are prone to problems such as failure to converge and falling into local optimum, and it is difficult to achieve effective scheduling

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  • AGV scheduling method based on ant colony and genetic algorithm
  • AGV scheduling method based on ant colony and genetic algorithm
  • AGV scheduling method based on ant colony and genetic algorithm

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0052] Such as figure 1 Shown: In order to effectively solve the collaborative work of many AGVs in automated storage and improve the efficiency of AGV scheduling, the AGV scheduling method of the present invention includes the following steps:

[0053] Step 1. For the distribution center and n customer nodes around the distribution center, a set N={0,1,2,...,n} is obtained, wherein node 0 is the distribution center, and the set of customer nodes is N c , N c ={1,2,...,n}, n is the number of client nodes;

[0054] For m dispatched vehicles, a vehicle set K is obtained, where K={1,2,...,m}, when dispatching, all vehicles must start from node 0 and return to node 0;

[0055] For client node set N c For any node i in , there is a time window [a i ,b i ], a i Indicates the earliest time when node i starts to accept goods, b i is the latest time for node i t...

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Abstract

The invention relates to an AGV scheduling method, in particular to an AGV scheduling method based on an ant colony and a genetic algorithm. A vehicle capacity factor and a time window factor are introduced into the ant colony algorithm to improve the ant state transition probability, and a pheromone volatilization factor is improved, so that the pheromone volatilization factor can be automatically adjusted along with a calculation process, meanwhile, a pheromone updating strategy is improved, and elite ants exceeding a globally optimal solution are rewarded. And finally, local optimization isperformed on an optimal solution obtained by the ant colony algorithm by using selection, crossover and mutation operators in the genetic algorithm. The algorithm convergence speed is increased, thesolution quality is improved, the defects that when a traditional optimization algorithm is used for path planning, the convergence speed is low, and local optimum is likely to happen can be obviouslyovercome, the solving efficiency of actual problems can be improved, and blindness of the iteration process is reduced.

Description

technical field [0001] The invention relates to an AGV scheduling method, in particular to an AGV scheduling method based on ant colony and genetic algorithm. Background technique [0002] With the development of modern industry and information service industry, human capital is becoming more and more precious, and people are increasingly aware of the importance of warehousing, freight and other logistics links to improve product profits. According to data, logistics, transportation and other links account for more than 50% of the cost of the entire manufacturing enterprise. Therefore, improving the efficiency of storage and transportation and giving full play to the performance advantages of the site have become the focus of major companies competing for development. [0003] The automated three-dimensional warehouse is an important part of the modern logistics system. The degree of intelligence of the automated three-dimensional warehouse has an important impact on the dev...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06N3/12G06Q10/06G06Q10/08
CPCG06N3/006G06N3/126G06Q10/06315G06Q10/06312G06Q10/087
Inventor 奚青陈曲燕陈晖周德强
Owner 无锡弘宜智能科技有限公司
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