Improving the Performance of Queueing Systems through Cross-Training

这项赠款为研究如何为工人分配任务提供资金，假设工人可以接受交叉培训，能够执行各种任务。 我们的目标是开发易于实现的，强大的，动态的工作分配政策，实现性能指标，如吞吐量，逗留时间和在制品的改进。 该研究还将寻求确定特定工作分配策略将最大受益的条件，量化由此产生的绩效改善的幅度，确定这些策略对改善绩效影响最大的方面，并调查结果对特定（建模和分析）假设的敏感性。 为了实现本项目的目标，组织将被建模为嵌入式系统，并使用几种方法进行分析（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）。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，如何交叉培训和动态工作分配策略可以用来提高组织的业绩，以及有多少好处，可以通过实施这些策略。 这项研究还有望提供定性的见解，管理人员可以用来实施简单的变化，在如何利用他们的组织的劳动力，可能会产生更好的性能。

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.