Computer second-class assembly line balance optimization method based on migration genetic algorithm

Abstract

The invention discloses a computer second-class assembly line balance optimization method based on a migration genetic algorithm, and relates to the technical field of assembly line balance. The method comprises migrating a feasible solution set of a known assembly line to an initial solution set of a balance problem of an assembly line to be optimized by utilizing assembly experience of similar assembly lines. Due to migration of the high-quality feasible solution, the method can effectively reduce the sensitivity of algorithm performance related to an initial value and parameters, and improve the lower limit of solving the assembly line balance problem local optimal feasible solution by the heuristic algorithm. Meanwhile, compared with an existing method for solving the balance problem of the assembly line, the method can accelerate the convergence speed, shorten the execution time and obtain a high-quality feasible solution more quickly. Therefore, the computer assembly line balance problem is optimized, the computer production cost is reduced, the computer assembly quality is improved, the computer production period is shortened, and development of the discrete manufacturing industry assembly technology is assisted.

Description

technical field [0001] The invention belongs to the technical field of assembly line balance, and in particular relates to a computer-based second-type assembly line balance optimization method based on migration genetic algorithm. Background technique [0002] With the rapid development of the economy, the assembly technology in my country's discrete manufacturing industry has been greatly developed. The problem of assembly line balance is a problem that production enterprises have always paid attention to, and it is also a good means of optimizing the allocation of enterprise resources. The assembly line balance problem is also different in different periods with the change of manufacturing technology, manufacturing mode and manufacturing process. At present, the assembly line has developed from traditional manual assembly to automated, modular station-based assembly, and finally forms a mobile assembly line in which each assembly station moves according to a certain rhyt...

AI Technical Summary

Problems solved by technology

The above methods have their own advantages and disadvantages. For example, the mathematical programming method can obtain the optimal analytical solution, but the amount of calculation is too large; the heuristic method can quickly obtain the answer, but the obtained solution is often suboptimal; the intelligent optimization method is widely used. It can efficiently solve NP-complete problems, but its parameters are difficult to control, the convergence speed is slow, the execution time is long, it is easy to fall into local optimum, the performance of the algorithm is related to the initial value and the parameters are sensitive, etc.

Industrial engineering methods are easy to implement improvement measures, but are limited by the experience and management level of on-site managers; simulation-based methods usually cannot fully simulate the production factors in the manufacturing assembly process

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