Product family assembly line balancing based on an improved genetic algorithm

Abstract

Product family assembly line (PFAL) is a mixed-model assembly line on which a family of similar products can be assembled at the same time. Aiming at the balance problem of PFAL, a balancing model for PFAL is established, and simultaneously an improved dual-population genetic algorithm is proposed. Firstly, through the characteristic analysis of PFAL, the tasks on PFAL are divided into three categories, namely the common, optional, and personality tasks. In addition, the correlation between the tasks is mainly considered. In the improved genetic algorithm, minimizing the number of stations, minimizing the load indexes between stations and within each station, and maximizing task-related degree are used as optimization objectives. In the initialization process, a method based on a TOP sorting algorithm is adopted for generating chromosomes. Furthermore, a new decoding algorithm is proposed to make up for the lack of the traditional decoding method, and individuals in the two populations are exchanged. Therefore, the search speed of the algorithm is accelerated, which shows good performance through classic tested problems. Finally, the effectiveness and feasibility of the method were validated by optimizing assembly line balancing of loaders.