Dynamic Group Scheduling: Theory and Applications

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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.