There is an abundance of prior research on the optimization of production systems, but there is a research gap when it comes to optimizing which components should be included in a design, and how they should be connected. To overcome this gap, a novel approach is presented for topology optimization of production systems using a genetic algorithm (GA). This GA employs similarity-based mutation and recombination for the creation of offspring, and discrete-event simulation for fitness evaluation. To reduce computational cost, an extension to the GA is presented in which a neural network functions as a surrogate model for simulation. Three types of neural networks are compared, and the type most effective as a surrogate model is chosen based on its optimization performance and computational cost. Both the unassisted GA and neural network-assisted GA are applied to an industrial case study and a scalability case study. These show that both approaches are effective at finding the optimal solution in industrial settings, and both scale well as the number of potential solutions increases, with the neural network-assisted GA having the better scalability of the two.

翻译：已有大量关于生产系统优化的研究，但在设计应包含哪些组件、如何连接组件这一优化问题上存在研究空白。为填补这一空白，提出了一种基于遗传算法的生产系统拓扑优化新方法。该遗传算法采用基于相似性的变异与重组机制生成子代，并通过离散事件仿真进行适应度评估。为降低计算成本，提出遗传算法的扩展方法，其中神经网络作为仿真的替代模型。对三种类型神经网络进行对比，并依据优化性能与计算成本选择最有效的替代模型类型。将未辅助遗传算法与神经网络辅助遗传算法分别应用于工业案例研究与可扩展性案例研究。结果表明，两种方法均能在工业场景中有效求得最优解，且随着潜在解数量的增加均展现出良好可扩展性，其中神经网络辅助遗传算法的可扩展性更优。