Assembly line multi-target modeling method, particle swarm algorithm and optimization scheduling method

A technology of multi-objective optimization and particle swarm algorithm, applied in computing, data processing applications, forecasting, etc., can solve problems such as difficulty in achieving overall optimization, and less research on reconfigurable assembly line scheduling optimization

Inactive Publication Date: 2015-12-16
HOHAI UNIV CHANGZHOU
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, there is an urgent need for a manufacturing assembly line to provide fast and effective reconfigurable functions. However, there are few researches on the scheduling optimization of reconfigurable assembly lines, and most of the traditional assembly system scheduling considers a single objective optimization, which is difficult to achieve the overall optimization goal

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  • Assembly line multi-target modeling method, particle swarm algorithm and optimization scheduling method
  • Assembly line multi-target modeling method, particle swarm algorithm and optimization scheduling method
  • Assembly line multi-target modeling method, particle swarm algorithm and optimization scheduling method

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Embodiment 1

[0063] Such as figure 1 As shown, the present invention provides a reconfigurable assembly line optimization scheduling method, comprising the following steps:

[0064] Step S1, building an assembly line multi-objective optimization model; and

[0065] Step S2, using the particle swarm optimization algorithm to perform multi-objective optimization design on the multi-objective optimization model of the assembly line, and screening the optimization results to reconstruct the assembly line.

[0066] Such as figure 2 As shown, specifically, the method for establishing an assembly line multi-objective optimization model in the step S1 includes the following steps:

[0067] Step S11, selecting a number of influencing factors affecting assembly; and

[0068] In step S12, corresponding models are respectively constructed for each influencing factor, and corresponding constraints are given.

[0069] Such as image 3 As shown, preferably, the particle swarm optimization algorithm...

Embodiment 2

[0081] Such as Figure 1 to Figure 3 As shown, the present invention also provides a method for building an assembly line multi-objective optimization model, comprising the steps of:

[0082] Step S11', select some influencing factors that affect assembly; and

[0083] In step S12', corresponding models are respectively constructed for each influencing factor, and corresponding constraints are given.

[0084] Further, in the step S11', select some influencing factors that affect the assembly, wherein

[0085] In Example 2, according to the production concept and actual needs of the reconfigurable assembly line, the three influencing factors of minimizing assembly line reconfiguration costs, production load balancing, and minimizing delay workload were selected to establish a multi-objective optimal scheduling model for reconfigurable assembly lines.

[0086] In the step S12', construct corresponding models respectively for each influencing factor, and provide the method for ...

Embodiment 3

[0111] Such as Figure 1 to Figure 3 As shown, on the basis of embodiment 2, the present invention also provides a kind of improved particle swarm algorithm for assembly line multi-objective optimization model, it is characterized in that, comprises the following steps:

[0112] Step S1', write the M-file to define the function of building the model;

[0113] Step S2', initializing the internal particle swarm and setting the external population;

[0114] Step S3', update the external population according to the dominance relationship, then sort the external population in descending order based on the individual crowding distance, and then delete the individuals exceeding the capacity;

[0115] Step S4', setting a new global optimal value according to the global optimal value update strategy;

[0116] Step S5', perform small-scale random mutation on the internal particle swarm, and then judge whether the maximum number of cycles is reached, if not, increase the number of iter...

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Abstract

The invention relates to an assembly line multi-target modeling method, a particle swarm algorithm and an optimization scheduling method, wherein the optimization scheduling method comprises the steps of S1 constructing a assembly line multi-target optimization model; S2 performing multi-target optimization design to the assembly line multi-target optimization model by the particle swarm algorithm and screening the optimization result to reconstruct the assembly line. According to the reconstructible assembly line optimization scheduling method, a crowding distance calculation method and an elite strategy are referred to; diversity maintenance and global optimal value update are conducted on the basis of individual crowding distance ordering; the complex fitness calculating process is avoided; and a small probability random mutation mechanism is introduced, thereby enhancing the global searching optimization capability gratly.

Description

technical field [0001] The invention belongs to the field of mechanical design automation, and in particular relates to an assembly line multi-objective modeling method, a particle swarm algorithm and an optimal scheduling method. Background technique [0002] In recent years, with the development of cloud manufacturing technology and the increasingly personalized market demand, manufacturing companies are facing many changes. In order to enhance their adaptability in the highly competitive market, they must reduce the preparation work and manufacturing equipment required for conversion. investment to improve product quality. Therefore, there is an urgent need for a manufacturing assembly line to provide fast and effective reconfigurable functions. However, there are few researches on the scheduling optimization of reconfigurable assembly lines, and most of the traditional assembly system scheduling considers a single objective optimization, which is difficult to achieve the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04
Inventor 苑明海程硕邓坤
Owner HOHAI UNIV CHANGZHOU
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