I-Corps: Translation Potential of a Real-Time, Optimized, Data-Driven Decision Making in Complex Systems

The broader impact of this I-Corps project is based on the development of a decision-making tool designed to optimize data-driven decision-making in complex systems across various industries. This technology has the potential to enhance operational efficiency, reduce costs, and minimize environmental impact in sectors such as manufacturing and logistics.

By enabling organizations to make optimized, real-time decisions, the tool can increase throughput, improve resource utilization, and streamline processes, leading to significant economic benefits. This innovation addresses a critical gap in existing tools, offering a scalable solution that adapts dynamically to real-world complexities.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a versatile, real-time, data-driven, decision-making application designed to optimize complex systems across various industries. This technology utilizes an advanced simulation and optimization tool that integrates reinforcement learning (RL) and mathematical programming to optimize decision-making in real-time.

The core technology simulates complex systems, allowing for the simultaneous optimization of production schedules, workforce allocation, and resource management. Initial research demonstrates that this application can significantly reduce computation time while achieving optimal outcomes, outperforming conventional methods. The technology’s adaptability allows it to be applied across multiple industries, making it a potential solution for optimizing operations in diverse environments.

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.