Recruitment in large organisations often involves interviewing a large number of candidates. The process is resource intensive and complex. Therefore, it is important to carry it out efficiently and effectively. Planning the selection process consists of several problems, each of which maps to one or the other well-known computing problem. Research that looks at each of these problems in isolation is rich and mature. However, research that takes an integrated view of the problem is not common. In this paper, we take two of the most important aspects of the application processing problem, namely review/interview panel creation and interview scheduling. We have implemented our approach as a prototype system and have used it to automatically plan the interview process of a real-life data set. Our system provides a distinctly better plan than the existing practice, which is predominantly manual. We have explored various algorithmic options and have customised them to solve these panel creation and interview scheduling problems. We have evaluated these design options experimentally on a real data set and have presented our observations. Our prototype and experimental process and results may be a very good starting point for a full-fledged development project for automating application processing process.
翻译:在大型组织中,征聘往往涉及面试大量候选人。这是一个资源密集和复杂的过程。因此,重要的是要高效率和高效力地进行这项工作。规划甄选过程包括几个问题,其中每一个问题都绘制出一个或另一个众所周知的计算问题。单独研究这些问题都是丰富和成熟的。然而,综合研究这一问题并不常见。在本文中,我们将应用处理问题的两个最重要的方面,即审查/询问小组的创建和面试时间安排。我们把我们的方法当作一个原型系统,并用它自动规划真实数据集的面试过程。我们的系统提供了明显比现有做法(主要是手册)更好的计划。我们探索了各种算法选择,并定制了它们来解决这些专门小组的创建和面试时间安排问题。我们用一个真实的数据集实验性地评价了这些设计选择,并提出了我们的意见。我们的原型和实验过程和结果可能是全面开发应用程序处理过程自动化的极好起点。