Stochastic programming is a mathematical technique that helps you make optimal decisions under uncertainty. It can help you plan, allocate, and optimize your resources in various industries, such ...

Stochastic programming has been applied to many domains and problems that involve uncertainty, such as production planning, portfolio optimization, energy management, and transportation and logistics.

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

Discover how stochastic programming can improve production planning and scheduling in the face of uncertain demand. Explore a multi-product, lotsizing and scheduling problem on parallel machines with ...

Discover the power of stochastic quadratic programming with imperfect probability distribution. Explore our paper on the innovative linear partial information (LPI) theory and a direct optimizing ...

In this work, we apply the sample average approximation (SAA) to general stochastic programs The SAA approach consists of approximating this problem by replacing all expectations with Monte-Carlo ...

Dynamic stochastic matching problems arise in a variety of recent ... (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming-based approach to ...