Dynamic Group Scheduling: Theory and Applications

在当今的商业世界中，管理人员生成和跟踪的数据比以往任何时候都多。这种信息收集技术已经改变了决策的方式；它与其说是直觉，不如说是数据分析。帮助这一运动的量化方法已成为循证决策实践中的关键工具。例如，2005年至2010年间，美国制造业的数据驱动型决策增加了两倍。尽管这些丰富的信息揭示了奇妙的洞察力，但我们对相互依赖的系统做出决定的能力仍处于初级阶段。此外，使用大量数据来提出驱动这些系统的管理建议的量化方法非常缺乏。*我的研究提案的目标是研究新的量化工具来管理具有不确定性的动态系统。许多现有的文献将这些系统分解成独立的、非交互的组件。每个组件负责处理一组任务；单独对每个组件执行分析。然后在原始的未分解系统中实现所得到的洞察力，其中组件之间的交互通常被忽略或以特别的方式组合。然而，在实践中，适当地分离组件可能是一项挑战。根据一起执行的任务和处理任务的顺序，系统可能会有不同的行为。*我的研究重点是开发量化方法，将相互依赖的系统作为一个凝聚力单位进行管理。它解决物流、医疗保健和制造方面的问题。在接下来的五年里，我将在我的随机建模和数学优化知识以及我分析复杂的真实世界过程的经验的基础上，为这些系统的管理员创建新的数学和计算工具。这项研究的资金将用于培训四名高素质的人员，使我能够在北美的几个会议上发表我的研究，并将相应的手稿提交给运筹学中影响较大的同行评议期刊。我提出的研究将对工业界和学术界做出有意义的贡献。此外，它还具有巨大的潜力，有助于将加拿大确立为数据驱动的决策制定量化方法的领导者之一。

In today's business world, managers generate and track more data than ever. This information gathering technology has changed the way decisions are made; it is less about intuition and more about data analysis. Quantitative methods that aid this movement have become key tools in the practice of evidence-based decision making. For example, data-driven decisions in US manufacturing tripled between 2005 and 2010. Although this abundance of information has uncovered fantastic insights, our ability to make decisions regarding inter-dependent systems is still in its infancy. Moreover, quantitative methods that use large amounts of data to make managerial recommendations that drive these systems are sorely lacking.******The objective of my research proposal is to investigate new quantitative tools to manage dynamic systems with uncertainty. Much of the existing literature decomposes these systems into separate, non-interacting components. Each component is responsible for processing a set of tasks; analysis is performed on each component in isolation. The resulting insights are then implemented in the original non-decomposed system, where the interactions between components are usually ignored or combined in an ad-hoc fashion. In practice, however, it may be challenging to appropriately separate components. The system may behave differently depending on which tasks are performed together and the sequence the tasks are processed in. ******My research focuses on developing quantitative methods to manage inter-dependent systems as one cohesive unit. It tackles problems in logistics, healthcare, and manufacturing. Over the next five years, I will build on my knowledge of stochastic modeling and mathematical optimization and my experience analyzing complex, real-world processes, to create new mathematical and computational tools for administrators of these systems. Funding for this research will be used to train four high-quality personnel, will allow me to present my research at several conferences in North America, and submit the corresponding manuscripts to high-impact, peer-reviewed journals in operations research. My proposed research will make a meaningful contribution to both industry and the academic community. Furthermore, it has great potential to help establish Canada as one of the leaders in developing quantitative methodologies for data-driven decision-making.