Reproducibility of experiments is a complex task in stochastic methods such as evolutionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps or software tools to help in this process. In this article, we propose a practical methodology to favor reproducibility in optimization problems tackled with stochastic methods. This methodology is divided into three main steps, where the researcher is assisted by software tools which implement state-of-the-art techniques related to this process. The methodology has been applied to study the double-row facility layout problem (DRFLP) where we propose a new algorithm able to obtain better results than the state-of-the-art methods. To this aim, we have also replicated the previous methods in order to complete the study with a new set of larger instances. All the produced artifacts related to the methodology and the study of the target problem are available in Zenodo.

实验的可重复性在随机方法（例如进化算法或元启发法）中是一项复杂的任务。文献中的许多作品都给出了有利于再现性的一般准则。然而，他们都没有提供一套实用的步骤或软件工具来帮助完成这个过程。在本文中，我们提出了一种实用方法，有利于随机方法解决的优化问题的可重复性。该方法分为三个主要步骤，研究人员得到软件工具的协助，这些软件工具实现了与该过程相关的最先进技术。该方法已应用于研究双排设施布局问题（DRFLP），我们提出了一种新算法，能够获得比最先进的方法更好的结果。为此，我们还复制了以前的方法，以便用一组新的更大实例来完成研究。所有与目标问题的方法和研究相关的生成工件都可以在 Zenodo 中使用。