In classical two-stage stochastic programming the expected value of the total costs is minimized. Recently, mean-risk models studied in mathematical finance for several decades have attracted attention in stochastic programming. We consider Conditional Value-at-Risk as risk measure in the framework of two-stage stochastic integer programming. The paper addresses structure, stability, and algori...

In this article, the design of a Supply Chain Network (SCN) consisting of several suppliers, production plants, distribution centers and retailers, is considered. Demands of retailers are considered stochastic parameters, so we generate amounts of data via simulation to extract a few demand scenarios. Then a mixed integer two-stage programming model is developed to optimize simultaneously two o...

A methodology is presented to include financial risk management in the framework of two-stage stochastic programming for planning under uncertainty. A known probabilistic definition of financial risk is adapted to be used in this framework and its relation to downside risk is analyzed. Using these definitions, new two-stage stochastic programming models that manage financial risk are presented....

in this paper, a stochastic two-stage model is offered for optimization of the day-ahead scheduling of the microgrid. system uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. for handling uncertainties, monte carlo simulation is employed for generation several scenarios and then a reduction method is used to d...

In this paper, a single-product, single-machine system under Markovian deterioration of machine condition and demand uncertainty is studied.  The objective is to find the optimal intervals for inspection and preventive maintenance activities in a condition-based maintenance planning with discrete monitoring framework. At first, a stochastic dynamic programming model whose state variable is the ...

This article aims to explain the Nested Benders algorithm for the solution of large-scale stochastic programming problems in a way that is intelligible to someone coming to it for the first time. In doing so it gives an explanation of Benders decomposition and of its application to two-stage stochastic programming problems (also known in this context as the L-shaped method), then extends this t...