Stochastic Dynamic Programming
Metron data scientists apply stochastic dynamic programming methods to solve incredibly large and complex planning problems.Join Our Team
Stochastic dynamic programming methods integrate probability, statistics, and analysis to solve some of the world’s toughest and most complex optimization problems.
In a complex environment, such as large-scale planning and scheduling, any decision we make can affect the entire system and limit the future choices we have available. Stochastic dynamic programming provides a robust mathematical methodology for deriving optimal solutions in these complex environments. Working through “backward induction,” we can measure a decision’s value based on how it changes the possible final outcome we can reach. Metron data scientists translate complex real-world problems into Markov decision processes (assigning incremental rewards along the path) to create a framework for choosing the optimal (or near-optimal) solution.