Learn what stochastic programming is, how it works, and how you can apply it in your industry to optimize your decisions and outcomes under uncertainty.

Learn the basics of stochastic programming, a method of optimization that deals with uncertainty using random variables and optimizes expected or worst-case performance.

The new approach for vendor selection problem was established under the stochastic environment. After establishing the traditional multi-objective programming model, through minimizing the optimistic ...

We call Equations (1)- (3) stochastic quadratic programming with recourse models under LPI. Chen established a similar stochastic quadratic programming model in [8] , but assumed that the probability ...

Stochastic programming incorporates such factors into the mathematical formulation. In the present paper, we consider a multi-product, discrete, lotsizing and scheduling problem on parallel machines ...

Mars rocket-powered descent results. In this work, we apply the sample average approximation (SAA) to general stochastic programs The SAA approach consists of approximating this problem by replacing ...

Multistage stochastic mixed-integer linear problems (MSMILPs) are non-convex, usually of large scale, and hard to solve. This calls for decomposition approaches to keep the solution process ...

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov ...

This papers investigates problems in pricing and optimizing sidecar and collateralized reinsurance portfolios, employing a stochastic programming approach to ...