Supply Chain Decision Making Framework Considering Uncertainty

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COVID-19 has exacerbated an already fragile supply chain network for many of today’s everyday goods, food supplies, specialty chemicals, fuels, electronics components, and pharmaceuticals. The pandemic tested the flexibility and resilience of global supply chains as major international corporations experienced personnel shortages and other unexpected disruptions to their operations. These challenges motivate a vision for a restructured supply chain of the future, characterized by flexibility and fast adaptability to abrupt changes. To accomplish this, the decision-making time horizon, enterprise complexity, and objectives of the enterprise must be considered when developing a strategy for controlling individual manufacturing units, scheduling tasks in a manufacturing plan to meet product demand, and planning at the enterprise management level to ensure that raw materials are available throughout the supply chain. This research program will develop methodologies and frameworks that target the modernization of the enterprise structure to account for uncertainty and more closely integrate different levels of the supply chain for purified gas and pharmaceutical manufacturing and plastics recycling operations. This will allow for more efficient use of resources, avoid unnecessary waste, and compensate for expected and unexpected events to avoid the breakdown of the supply chain. The research will take place at the University of Delaware and will provide the funds to educate graduate and undergraduate students in this interdisciplinary domain. The PI has a long history of promoting women and underrepresenting minorities in their work. Results from this research will be translated into software tools useful to industry for making better supply chain decisions. Previous attempts to integrate process models, scheduling methods, planning problems and supply chain optimization focused on small scale benchmark problems and were based either on using intuition to incorporate full-scale representations of the lower-level problems into higher levels or used mathematical simplifications of the lower levels to facilitate integration. In this research program, a new approach to integrating decision-making processes is proposed that leverages the large amount of information that typically is available in enterprises and generates decision-making strategies that account for uncertainty in a computationally tractable manner. The PI has extensive expertise in the integration of planning, scheduling, and control problems, as well as in the areas of feasibility analysis and uncertainty quantification. It is proposed that integration and optimization can be achieved by first defining mathematical models for the optimization of each decision-making stage, and then identifying constraints that are dependent on lower-level problems. Among other factors, the feasibility of the lower-level problems is identified as essential information that must be incorporated in the higher-level decision. These constraints are usually complex in form and cannot be accounted for without increasing the dimensionality of the optimization problem and generating intractable formulations. Therefore, it will be demonstrated that data-driven models can be used to obtain simpler forms for the interdependent constraints. These models will be created with the data available from the enterprise, leveraging the potential of big-data analytics and the internet of things. The effects of uncertainty are key to the performance of optimal decisions in the supply chain. In this research program, strategies to minimize the threats posed by operational and disruption uncertainties will be developed. Computational complexity of the final optimization problem will be controlled through the use of decomposition approaches and a rolling horizon strategy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.