Improving the Performance of Queueing Systems through Cross-Training

这项拨款为一项研究提供资金，研究如何将工人分配到任务中，假设工人可以交叉训练以有能力执行各种任务。目标是开发易于实现的、健壮的和动态的工作分配策略，以实现性能度量的改进，例如吞吐量、停留时间和在处理工作。研究还将寻求确定特定工作分配策略将产生最大效益的条件，量化由此产生的绩效改进的幅度，确定这些策略中对改进绩效影响最大的方面，并调查结果对所做的具体（建模和分析）假设的敏感性。为了实现这个项目的目标，组织将被建模为排队系统，并使用几种方法（即，马尔可夫链分析，马尔可夫决策过程，随机比较，流体松弛和模拟）进行分析。如果成功的话，这项研究的结果预计将包括详细的指导方针，说明如何使用交叉培训和动态工作分配策略来提高组织的绩效，以及通过实施这些策略可以获得多少利益。这项研究也有望提供定性的见解，管理者可以使用这些见解来实现简单的变化，即如何利用组织的劳动力，这可能会提高绩效。

This grant provides funding for a study of how workers should be assigned to tasks, assuming that the workers can be cross-trained to capably perform a variety of tasks. The goal is to develop easily implementable, robust, and dynamic work assignment policies that achieve improvements in performance measures such as throughput, sojourn time, and work in process. The research will also seek to identify the conditions under which particular work assignment strategies will be of greatest benefit, quantify the magnitude of the resulting performance improvements, determine the aspects of these strategies that have the greatest impact towards improved performance, and investigate the sensitivity of the results to the specific (modeling andanalysis) assumptions made. In order to achieve the goals of this project, organizations will be modeled as queueing systems and analyzed using several methodologies (i.e., Markov chain analysis, Markov decision processes, stochastic comparisons, fluid relaxation, and simulation).If successful, the results of this research are expected to include detailed guidelines for how cross-training and dynamic work assignment strategies can be used to improve an organization's performance and for how much benefit can be obtained by implementing these strategies. This research is also expected to to provide qualitative insights that managers can use to implement simple changes in how their organization's workforce is utilized that are likely to yield improved performance.