Part supplier multi-target preferable selection method orienting bearing manufacturing enterprises

Abstract

The invention provides a part supplier multi-target preferable selection method orienting bearing manufacturing enterprises. The degree of capacity of each expert is acquired from historical samples through a neural network training method by considering objective difference of the experience levels and the knowledge levels of different experts; the degree of capacity of the experts is introduced and then a multi-order fuzzy rough set integrated analytic hierarchy process with combination of an analytic hierarchy process in which fuzzy numbers replace accurate numbers and rough sets is provided so that combined processing of multiple experts for multiple part suppliers on the score result of a single indicator is realized; a multi-weight information integration model is established, and integration weights of the evaluation indicators are solved; and the part suppliers are ranked according to the indicator value of each evaluation indicator and the integration weights of the indicators of multiple part suppliers, and the optimal part supplier is obtained.

Description

[0001] 1. Field [0002] The invention relates to the field of supplier selection in enterprise supply chain management, in particular to a multi-objective optimization method for parts suppliers oriented to bearing manufacturing enterprises. 2. Background technology [0003] Bearings are mainly composed of rollers, rings, sealing rings, cages, dust covers and other parts. With the increasingly fierce market competition, bearing manufacturers are increasingly concentrating on their core business, while external suppliers provide a large number of parts, such as balls, sealing rings, etc. These purchased parts have a direct impact on the quality of finished products, how to optimize parts suppliers has become the key for bearing manufacturers to improve their market competitiveness. [0004] For the selection of parts suppliers of bearing manufacturing enterprises, the current evaluation method is mainly based on the quantification and weighting calculation of specific indicat...

AI Technical Summary

Problems solved by technology

[0004] For the selection of parts suppliers of bearing manufacturing enterprises, the current evaluation method is mainly based on the quantification and weighting calculation of specific indicators, but different bearing manufacturing enterprises have obvious differences in the degree of attention to the evaluation indicators of parts suppliers, this method is only applicable For a specific type of bearing manufacturing enterprise, the generalizability is poor; when index weighting, a single weight or the average of multiple weights is used to calculate the index weight, ignoring the combination relationship between multiple weights

In addition, the expert experience method is also applied to a certain extent. This method takes the experience ability of experts as the core and has some merits. However, direct application often makes the optimization effect poor; there are differences in the experience level and knowledge level of experts. The selection results of different experts are very different; it is difficult to take into account many evaluation indicators

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