Abstract
Aiming at the lack of search depth of traditional genetic algorithm in automobile assembly line balance optimization, an improved genetic algorithm based on bagging integrated clustering is proposed for balance optimization. Through the integrated learning of several K‐means algorithm based learners through bagging, a population clustering analysis method based on bagging integrated clustering algorithm is established, and then, a dual objective automobile assembly line balance optimization model is established. The population clustering analysis method is used to improve the intersection link of genetic algorithm to improve the search depth. The effectiveness and search performance of the improved genetic algorithm in solving the double objective assembly line balance problem are verified in an example.
Highlights
In today’s global economy, every manufacturing company is competing fiercely in an open, continuously changing, and unpredictable global market
In order to improve the search depth of the genetic algorithm, I established a bagging integrated clustering method to analyze the kinship between individuals in the population, and based on this method, I improved the crossover link of the genetic algorithm to improve the search depth of the algorithm and obtain a better feasible solution in the biobjective assembly line equilibrium optimization problem
The improved genetic algorithm based on hormone regulation mechanism and the selection, crossover, and variation operators are designed to solve the model of mixed assembly line balancing problem with one station and multiple products, which improves the performance of the algorithm [6]
Summary
In today’s global economy, every manufacturing company is competing fiercely in an open, continuously changing, and unpredictable global market. To improve the competitiveness of enterprises, automotive OEMs have commonly adopted the mixed-flow manufacturing technology, whose production operation control is based on the famous Just In Time (JIT) system [1] and Toyota Production System (TPS) [2]. The mixed-flow manufacturing system has improved and upgraded the traditional manufacturing system in many aspects such as flexible process routes, equipment layout, and inventory reduction, in order to adapt to Wireless Communications and Mobile Computing the changing market demand, the production mode adopts a multivariety and small-lot approach, and the system must be constantly adjusted according to the changes in demand, so the system cannot remain stable for a long time, and the production varieties brought by the production of new products and discontinuation of old products are inevitable [4]. In order to improve the search depth of the genetic algorithm, I established a bagging integrated clustering method to analyze the kinship between individuals in the population, and based on this method, I improved the crossover link of the genetic algorithm to improve the search depth of the algorithm and obtain a better feasible solution in the biobjective assembly line equilibrium optimization problem
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