Princeton Optimization

It’s Usually Not the Math: Risks in Implementing Optimization and AI Into Production

Following is a lightly edited excerpt of Steve’s presentation at the Joint Mathematics Meeting in January 2021, part of a special session, “Transformation Through Advanced Analytics.” I am pleased to be speaking on the topic of The Princeton 20: the most common risk factors in implementing optimization AI into production. As we discuss project risk, […]

Rapidly Developing Optimizing Simulations and Overcoming Barriers to Implementation

We define optimizing simulation as the application of optimization principles to simulation, taking it from predictive analytics to prescriptive analytics. Optimization is layered on top of inputs and a simulation to determine optimal policies, decisions, and even operating parameters based on key KPIs. This allows us to simulate environments, facilities, and different decisions that people […]