Collaborative Research: Coordinating Offline Resource Allocation Decisions and Real-Time Operational Policies in Online Retail with Performance Guarantees

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Many industries must intermittently make tactical resource allocation decisions that constrain the effectiveness of subsequent operational decisions using these resources. For example, online retailers must decide how to distribute their available inventory among geographically distributed warehouses, influencing individual order fulfillment decisions. In current practice the resource allocation decisions do not consider the nature of the operational decisions, which can result in significant losses in efficiency. This research project will contribute to the economic welfare of the nation by developing a general strategy for these problems that can be customized to different problem settings, providing a context-independent coordination mechanism that can be applied to many industries. The educational and outreach activities will involve students in hands-on optimization projects, create new education programs in datacentric decision-making, and address the food deserts problem in urban areas by viewing the problem from the lens of coordinating resource allocation decisions with real-time operations.The current state of knowledge does not provide efficient algorithms for coordinating tactical resource allocation decisions with the operational decisions that use those resources. There are two main reasons for this gap: computing the optimal operational policy often requires solving a high-dimensional dynamic program, and the optimal value functions may lack structure, providing no clear basis for resource allocation decisions. This project will build a general approximation framework for coordination that is independent of the application setting. The framework uses surrogate functions to develop an upper bound on the performance of the optimal operational policy and a lower bound on that of an approximate operational policy. Bounding the relative gap between the two surrogates will yield a performance guarantee for the coordination problem. Selection of appropriate surrogate functions will allow application of the approximation framework to different applications, providing efficient approximate solutions with provable performance guarantees. To achieve such solutions, ideas from combinatorial and dynamic optimization, as well as discrete choice and price response modeling will be combined. The resulting algorithms will be tested on several publicly available datasets.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.