Fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method

Abstract

The invention discloses a fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method. The method comprises: a priority constraint relationship between parts is expressed by using a priority sequence graph; a priority relationship matrix, an integrated interference matrix, a station capability list, and an assembling information table are constructed; and an interference relationship between parts as well as a relationship between stations is described. A coding system of a fruit fly algorithm is provided; and three searching stages including an odour searching stage, a vision searching stage, and a cooperative searching stage of the fruit fly are designed by considering the local and global searching capabilities of the fruit fly algorithm. A fitness function expression close to practical engineering is put forward by considering influences of an assembling operation cost, a replacement tool cost, a clamping changing cost and a transport cost comprehensively. According to a priority sequence matrix, initial sequence evolution is guided; and a product assembling sequence and a station assembling sequence are optimized by using the fruit fly optimization algorithm.

Description

technical field [0001] The invention relates to the technical field of product assembly sequence planning, in particular to a multi-station assembly sequence planning method based on a fruit fly optimization algorithm. Background technique [0002] Assembly sequence planning (Assembly Sequence Planning, ASP) is an important link in the product manufacturing process, and the assembly quality directly affects the performance of the product. As the pressure of market competition continues to intensify, people hope that products not only meet performance requirements, but also occupy an absolute advantage in product cycle and product cost among peers. Related studies have shown that a good assembly sequence can reduce manufacturing costs by 20% to 40%, and increase production efficiency by 100% to 200%. Therefore, assembly sequence planning occupies a very important position in the product manufacturing process. [0003] Scholars at home and abroad have done a lot of research ...

AI Technical Summary

Problems solved by technology

However, the existing optimization algorithms mainly focus on completing product assembly planning tasks at a single station, while there are few studies on multi-station and multi-cost optimization that are closer to the actual assembly environment; moreover, the general optimization algorithm needs to consider many parameters. The influence between is relatively large, which affects the quality of algorithm execution

Images