基于FBS的空间进化算法的不等形面积布局优化研究

Research on Optimization of Unequal Area Layout Based on FBS Spatial Evolution Algorithm

多目标车间布局优化是现代制造业发展的必然趋势。通过综合考虑生产效率、成本控制、工作环境和员工满意度等多方面因素,制定科学合理的布局方案,将有助于提升企业的整体竞争力和可持续发展能力。然而,传统的多目标进化算法在布局优化解决方案的融合性和多样性方面面临着巨大的挑战。本文提出了一种基于柔性隔间结构的空间进化算法(ISEA)来求解具有多目标的设施布局问题。首先,创建了空间配置库,并使用进化操作(选择、交叉和变异)来产生新的配置,通过引入配置组半径d来控制ISEA中解的收敛性。其次,将最近和最远候选解方法与快速非主导排序相结合,选择帕累托最优解,以保证所得解的多样性。实验在8个不同的代表性实例和3个参数指标上进行了实验。与现有的MOEAs相比,ISEA能够找到更好的结果并具有更好的性能。数值实验验证了ISEA求解多目标布局优化问题的有效性。Multi-objective workshop layout optimization is the inevitable trend of the development of modern manufacturing industry. Making a scientific and reasonable layout plan by comprehensively considering many factors such as production efficiency, cost control, working environment and employee satisfaction will help to enhance the overall competitiveness and sustainable development ability of enterprises. However, the traditional multi-objective evolutionary algorithm faces great challenges in the integration and diversity of layout optimization solutions. In this paper, a spatial evolution algorithm (ISEA) based on flexible compartment structure is proposed to solve the facility layout problem with multiple objectives. Firstly, the spatial configuration library is created, and new configurations are generated by evolutionary operations (selection, crossover and mutation). The convergence of solutions in ISEA is controlled by introducing the radius d of configuration group. Secondly, the nearest and farthest candidate solution method is combined with fast non-dominant sorting to select Pareto optimal solution to ensure the diversity of the obtained solutions. Experiments were carried out on 8 different representative examples and 3 parameters. Compared with existing MOEAs, ISEA can find better results and has better performance. Numerical experiments verify the effectiveness of ISEA in solving multi-objective layout optimization problems.