基于改进遗传算法的混装线多目标优化

Multi-objective optimization for mixed-model assembly line balancing problem based on improved genetic algorithm

在考虑产品需求速率的前提下,提出了调整加工成本的新方法,建立了混流装配线平衡问题的多目标优化模型。设计了基于自然数序列和拓扑排序的改进遗传算法对模型进行求解,改进交叉、变异操作来保护优秀基因,提出了种群扩张机制。对经典问题的计算试验结果表明,改进遗传算法在降低生产节拍的同时能优化产品加工成本,在求解效率和求解质量方面有显著的成效。

Under the consideration of product demand ratio, a new method to adjust the operating costs was pro- posed, and the multi-objective optimization model for mixed-model assembly line balancing problem was formulated. An improved genetic algorithm base on natural number code and topological sorting was designed to solve the model. The crossover and mutation operation of standard genetic algorithm was improved to protect excellent genes, and the population expansion mechanism was proposed. Through testing for benchmark problems, the -results showed that the proposed algorithm could decrease the cycle time and optimize product processing costs, and the improved genetic algorithm also had a significant effect in decrease computing time.