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AGV configuration optimization method based on simulation particle swarm optimization

A particle swarm algorithm and configuration optimization technology, applied in the direction of calculation, calculation model, data processing application, etc., can solve the problems of abnormal difficulty, high AGV configuration cost, unstable production system efficiency, etc., to reduce blockage, reduce cost, improve The effect of production efficiency

Pending Publication Date: 2022-07-29
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the defects of high cost of AGV configuration described in the above prior art, it will become extremely difficult to simulate a production system with an analytical method in a large-scale random production system, and the efficiency of the production system is unstable, the present invention provides a simulation-based AGV configuration optimization method based on particle swarm optimization

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  • AGV configuration optimization method based on simulation particle swarm optimization
  • AGV configuration optimization method based on simulation particle swarm optimization
  • AGV configuration optimization method based on simulation particle swarm optimization

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Embodiment 1

[0088] like Figure 1-Figure 3 As shown, the present invention provides an AGV configuration optimization method based on a simulated particle swarm algorithm, which specifically includes the following steps:

[0089] S1. Build a simulation model of the actual production environment.

[0090] S2. Establish a mathematical model for AGV configuration optimization and set constraint parameter conditions.

[0091] S3. Use the simulation model to improve the particle swarm algorithm to obtain the improved particle swarm algorithm; calculate the fitness value of the particle by using the constraint parameter condition.

[0092] S4. Use the improved particle swarm algorithm to solve the mathematical model of the AGV configuration optimization, and obtain the optimized AGV configuration.

[0093] First build a simulation model that simulates the production process of the production system, establish a mathematical model for AGV configuration optimization and set constraint parameter...

Embodiment 2

[0165] Based on the above Embodiment 1, this embodiment describes in detail the specific process of using the simulation model to improve the particle swarm algorithm.

[0166] First, using the monotonic characteristic to obtain the required quantity of each AGV when only one AGV is considered through simulation, the obtained quantity is set as the upper bound of the solution space of this type of AGV.

[0167] Further, initialize the particle swarm, realize the data exchange between Plant simulation and pycharm according to the TCP / IP protocol, and then input the upper bound obtained above into the particle swarm algorithm as the upper bound of the solution space, and the lower bound is set to 0, that is, it is not used. Class AGV. Next, the position and velocity within a certain range are generated in the solution space and assigned to each particle to obtain the initial population, and the position and velocity of the ith particle in the particle population are recorded.

...

Embodiment 3

[0175] like Figure 4 As shown, this embodiment provides an AGV configuration optimization system based on a simulated particle swarm algorithm, characterized in that the system includes: a memory and a processor, and the memory includes a program for an AGV configuration optimization method based on the simulated particle swarm algorithm , the AGV configuration optimization method program based on the simulated particle swarm algorithm is executed by the processor to achieve the following steps:

[0176] S1. Build a simulation model of the actual production environment;

[0177] S2. Establish a mathematical model for AGV configuration optimization and set constraint parameter conditions;

[0178] S3, using the simulation model to improve the particle swarm algorithm to obtain the improved particle swarm algorithm; using the constraint parameter condition to calculate the fitness value of the particle;

[0179] S4. Use the improved particle swarm algorithm to solve the mathe...

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Abstract

The invention discloses an AGV configuration optimization method based on a simulation particle swarm algorithm. The AGV configuration optimization method comprises the following steps: constructing a simulation model of an actual production environment; establishing a mathematical model for AGV configuration optimization and setting constraint parameter conditions; improving a particle swarm algorithm by using the simulation model to obtain an improved particle swarm algorithm; calculating fitness values of particles by adopting the constraint parameter conditions; and solving the mathematical model for AGV configuration optimization by using an improved particle swarm algorithm to obtain optimized AGV configuration. Compared with a traditional AGV configuration method, the AGV configuration method has the advantages that the improved particle swarm optimization of the embedded simulation model is used for optimizing the AGV configuration model, a scientific analysis method and decision basis are provided for improvement of a production system, blockage in the production system is reduced, system operation is more efficient, and the production cost is reduced.

Description

technical field [0001] The invention relates to the field of AGV configuration optimization, and more particularly, to an AGV configuration optimization method based on a simulated particle swarm algorithm. Background technique [0002] In recent years, with the rapid development of technology, the frequency of product updates has been accelerated, and the service cycle of products has been shortened. Mass customized production has become a common production method for many enterprises. Personalized customized production is the development direction of the entire manufacturing industry in the future, and customized equipment production is a resource-oriented engineering-to-order (ETO) production method. Customized production methods generally have significant characteristics such as multiple optional process paths, order delivery time constraints, and randomness of production environments, which put forward higher requirements for the flexibility of production systems, and p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q10/08G06Q50/04G06N3/00
CPCG06Q10/04G06Q10/06313G06Q10/067G06Q10/083G06Q50/04G06N3/006
Inventor 张惠煜王松龄陈庆新毛宁
Owner GUANGDONG UNIV OF TECH
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