A reciprocated result using an approach of multiobjective stochastic linear programming models with partial uncertainty International Journal of Mathematics in Operational Research, 2015 [12] ...

1 Introduction Stochastic programming, also known as stochastic optimization (Birge and Louveaux, 2011), is a mathematical framework to model decision-making under uncertainty. The origin of ...

Linear programming (LP) is a popular method for optimization of a wide range of applications because of its simplicity and availability. However, LP, in its classic form, is not equipped to handle ...

In this paper, we propose two kinds of fuzzy approaches to obtain a satisfactory solution for multiobjective stochastic linear programming problems, in which the criteria of probability maximization ...

This paper gives an algorithm for L-shaped linear programs which arise naturally in optimal control problems with state constraints and stochastic linear programs (which can be represented in this ...

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 is the “wait and see” problem of stochastic linear programming. Explicit results for the distribution problem are extremely difficult to obtain; indeed, previous results are known only if the ...

Indeed, we focus in this study on proposing an exact method to optimize two preference functions over the efficient set of a Multi-objective Stochastic Integer Linear Programming (MOSILP) problem in ...

pp. 145On the Equivalence in Stochastic Programming with Probability and Quantile Objectives pp. 159Transport and Inventory Planning with Discrete Shipment Times pp. 163Expected Total Cost Minimum ...