Operation shop bottleneck recognition method based on cluster analysis and multiple attribute decision making

Abstract

The invention provides an operation shop bottleneck recognition method based on a cluster analysis concept and a multiple attribute decision making theory. The method comprises the following steps of 1, utilizing dispatching optimization scheme as input of bottleneck recognition, determining feature attributes of a bottleneck recognition device and calculating the feature attribute values of the device according to the dispatching optimization result; 2, acquiring clustering clusters of the device under different distances and a parent-child relationship dendritic structure chart thereof on the basis of the similarity of a characteristic attribute excavating machine of the device by utilizing a hierarchical clustering method; 3, determining cluster centers of two sub-clusters of a final clustering cluster, comparing the attribute values of the cluster centers on the basis of a TOPSIS method and determining bottleneck clusters containing few device members; and 4, sequentially comparing sub-clusters of the bottleneck clusters and gradually obtaining main bottleneck clusters of different orders. According to the embodiment of the invention, the method provided by the invention can be used for solving the multi-bottleneck recognition problem which cannot be solved by the existing method.

Description

technical field [0001] The invention relates to the technical field of job shop bottleneck identification, in particular to a job shop bottleneck identification method based on cluster analysis and multi-attribute decision-making. Background technique [0002] The current bottleneck identification method in the job shop is mainly based on the index method, which can be divided into four types of bottleneck identification methods: equipment, work in progress, effective output, and time. [0003] Equipment category: Most of these bottleneck identification methods use indicators such as machine load and machine processing capacity to directly identify system bottlenecks, or identify bottleneck machines based on indicators such as machine utilization rate and machine capacity busy-idle rate based on historical data and simulation data. Define the machine with the worst processing capacity as the bottleneck of the system, or define the machine with the largest load as the bottlen...

AI Technical Summary

Problems solved by technology

[0009] 2) Insufficient use of bottleneck identification indicators

[0011] 3) There are few studies on multi-bottleneck identification, and most of the existing multi-bottleneck divisions have no scientific basis

Some literature selects the top 30% machines whose index value is greater than the average index value as the bottleneck machine. Since the range is selected artificially, there is no scientific basis, and the rationality cannot be guaranteed.

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