Improved unified particle swarm algorithm-based mechanical part machining pipeline scheduling method

A particle swarm algorithm, a technology of mechanical parts, applied in general control systems, control/regulation systems, instruments, etc., can solve problems such as combinatorial optimization that cannot be directly applied to discrete

Inactive Publication Date: 2019-11-19
余姚市浙江大学机器人研究中心 +1
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  • Claims
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AI Technical Summary

Problems solved by technology

However, this algorithm can only be used for continuous space search, and

Method used

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  • Improved unified particle swarm algorithm-based mechanical part machining pipeline scheduling method
  • Improved unified particle swarm algorithm-based mechanical part machining pipeline scheduling method
  • Improved unified particle swarm algorithm-based mechanical part machining pipeline scheduling method

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Embodiment

[0136] The method of the present invention is used for the dispatching of workpiece processing order below, can be the similar workpieces such as bearing, assume that its processing time is as shown in the table below:

[0137]

[0138]

[0139] The method of discrete unified particle swarm optimization based on dynamic neighborhood and comprehensive learning for this pipeline scheduling problem is as follows:

[0140] 1. Read in the operation time of mechanical parts processing;

[0141] 2. Initialize the population, including setting the maximum number of iterations G max =50, the population size of the particle swarm N=20, the population reorganization threshold g=5, the learning item reorganization threshold m=3, the range of particles constituting the global learning item p=0.2; use the NEH method to initialize the position x of each particle i ;Set the initial individual historical optimal solution as the initial position; clear the stagnation count flag of partic...

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Abstract

The invention discloses a mechanical part machining pipeline scheduling method of employing a dynamic neighborhood and comprehensive learning-based discrete unified particle swarm algorithm. The mechanical part machining pipeline scheduling method comprises the following steps of reading operation time of mechanical part machining; carrying out population initialization; calculating a fitness value of each particle and sorting the particles; updating an optimal position, an individual optimal position, a global optimal position and a learning item in each particle neighborhood; carrying out global searching by adopting dynamic neighborhood and comprehensive learning-based discrete unified particle swarm optimization; carrying out elite learning strategy-based local searching; regrouping populations after a certain number of times; and recombining the learning items when updating of a global optimal solution stalls. According to the mechanical part machining pipeline scheduling method,the limitation of the dynamic neighborhood and comprehensive learning-based unified particle swarm optimization in the field of production scheduling is improved; the defects that standard particle swarm optimization depends too much on parameters and is easy to fall into local optimum are overcome; the mechanical part machining pipeline scheduling method has the characteristics of high search accuracy, a high convergence rate and the like; and furthermore, the mechanical part machining pipeline scheduling method is relatively wide in application range and can be extended to the fields of manufacturing and process industries.

Description

technical field [0001] The invention relates to a pipeline scheduling method for machining mechanical parts, in particular to a pipeline scheduling method for machining mechanical parts based on a discrete unified particle swarm algorithm based on dynamic neighborhood and comprehensive learning. Background technique [0002] The assembly line scheduling of mechanical parts processing belongs to the assembly line scheduling. It is a typical production scheduling problem that is widely studied and applied. At the same time, it has many variables and is a complex combinatorial optimization problem that is strongly NP-hard. Reasonable scheduling is the core content of production management. With the increasingly intensified market competition and the diversification and individualization of customer needs, the production of enterprises is gradually developing in the direction of "multiple varieties, small batches, fast delivery, and less inventory". Scheduling is even more impor...

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Application Information

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IPC IPC(8): G05B13/02
CPCG05B13/024
Inventor 张建明王文靖
Owner 余姚市浙江大学机器人研究中心
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