Facility layout aims to arrange a set of facilities in a site. The main objective function is to minimize the total material handling cost under production-derived constraints. This problem has received much attention during the past decades. However, these works have mainly focused on solving a 2D layout problem, dealing with the footprints of pieces of equipment. The obtained results have been then adapted to the real spatial constraints of a workshop. This research work looks to take account of spatial constraints within a 3D space from the very first steps of problem solving. The authors use a approach by combining a genetic algorithm with A*, 〈GA,A*〉 research. The genetic algorithm generates possible arrangements and A* finds the shortest paths that products must travel in a restricted 3D space. The application allows to converge to a layout minimizing the total material handling cost. This approach is illustrated by its application on an example inspired by a valve assembly workshop in Tunisia and the results are discussed from two points of view. The first one consists in comparing the effect of the choice of the distance measurement technique on the handling cost. For this purpose, the results of the application of 〈GA,A*〉 are compared with those obtained by combining the genetic algorithm and two of the most commonly used distance measurements in the literature of the discipline, namely the Euclidean distance, 〈GA,Euclidean〉, and the rectilinear distance, 〈GA,rectilinear〉. Our results show that the proposed approach offers better results than those of 〈GA,rectilinear〉 whereas they are not as good as those obtained by the 〈GA,Euclidean〉 approach. The effectiveness of the 〈GA,A*〉 approach is then studied from the perspective of the effect of the algorithm used for the generation of candidate arrangements. The final results obtained from the application of 〈GA,A*〉 are then compared with those of the approach combining particle swarm optimization and A*, 〈PSO,A*〉. This comparison shows that the 〈GA,A*〉 approach obtains better results. Nevertheless, its convergence speed is lower than that of 〈PSO,A*〉. The paper ends with some conclusions and perspectives.

设施布局旨在在站点中安排一组设施。主要目标功能是在生产引起的约束下使总物料搬运成本最小化。在过去的几十年中，这个问题受到了很多关注。但是，这些工作主要集中于解决2D布局问题，处理设备的占地面积。然后将获得的结果适应车间的实际空间约束。这项研究工作着眼于从解决问题的第一步开始就考虑到3D空间中的空间限制。作者使用了一种将遗传算法与A *，<GA，A *>研究相结合的方法。遗传算法生成可能的排列，并且A *查找产品必须在受限的3D空间中传播的最短路径。该应用程序允许收敛到使材料处理总成本最小化的布局。通过在突尼斯的阀门装配车间的启发下将其应用到一个实例中来说明这种方法，并从两个角度讨论了结果。第一个是比较测距技术选择对处理成本的影响。为此，将＆lt; GA，A *＆gt;的应用结果与通过遗传算法和该学科文献中两个最常用的距离测量相结合而获得的结果进行了比较，即欧几里德距离<GA，欧几里得〉，直线距离〈GA，rectilinear〉。我们的结果表明，所提出的方法比〈GA，rectilinear〉的方法具有更好的结果，但不如〈GA，rectilinear〉的方法好。欧几里德〉方法。然后从用于生成候选排列的算法的效果的角度研究＆lt; GA，A *＆gt;方法的有效性。然后将应用〈GA，A *〉获得的最终结果与结合粒子群优化和A *，〈PSO，A *〉的方法的结果进行比较。这种比较表明，〈GA，A *〉方法获得了更好的结果。但是，它的收敛速度低于<PSO，A *>。本文以一些结论和观点作为结尾。〈PSO，A *〉。这种比较表明，〈GA，A *〉方法获得了更好的结果。但是，它的收敛速度低于<PSO，A *>。本文以一些结论和观点作为结尾。〈PSO，A *〉。该比较表明，〈GA，A *〉方法获得了更好的结果。但是，它的收敛速度低于<PSO，A *>。本文以一些结论和观点作为结尾。