It has become increasingly challenging to match supply and demand as firms have extended their supply chains to a global scale. One major concern for many multi-national firms with global supply networks is supply uncertainty, especially supply disruption. Another big concern is the reduced responsiveness due to longer lead times. Dual-sourcing has been commonly implemented in practice as an effective strategy to mitigate supply risks and provide a tradeoff between cost and responsiveness. This practice calls for advanced dual-sourcing models that incorporate supply uncertainty and large replenishment lead times simultaneously, and the development and analysis of effective inventory strategies. The objective of this research is to provide a comprehensive understanding of complex dual-sourcing systems as well as provide new insights and analytical tools to handle this level of complexity. The research has the potential to provide new tools and methodologies to effectively manage value chain systems and thus improve companies' competitive advantage. In addition, it will provide educational opportunities for graduate and undergraduate students, and has the potential to broaden participation of underrepresented groups and minorities through the Multi-Cultural Engineering Recruitment for Graduate Education Program (MERGE) at the University of Illinois.
If successful, the results of this project will have a significant impact on the theory and practice of dual-sourcing inventory optimization. First, the research will develop novel transformation techniques to convert non-convex minimization problems to equivalent convex minimization problems applicable to complex dual-sourcing models. Second, the research will, provide theoretic support of several heuristics widely used in practice, design new efficient algorithms, and explore potential applications of the advanced methodologies developed under the auspices of this project in other problems.
