Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochasti...

dam reservoirs supplies water resources for drinking, agriculture, and industry almost in all parts of iran. lack of proper utilization rules and unbalanced demands versus available water has led to many problems regarding to ineffective use of the water resources. precipitation and flow rate are stochastic phenomena. therefore, multi-step processes related to the water use efficiency should be...

Linear stochastic programming problems with first order stochastic dominance (FSD) constraints are non-convex. For their mixed 0–1 linear programming formulation we present two convex relaxations based on second order stochastic dominance (SSD). We develop necessary and sufficient conditions for FSD, used to obtain a disjunctive programming formulation and to strengthen one of the SSD-based rel...

performance evaluation of electricity distribution units is an important issue between researchers and regulators. classic data envelopment analysis models with deterministic data have been used by many authors to measure efficiency of power distribution units in different countries. however, data envelopment analysis with stochastic data are rarely used to measure efficiency of distribution co...

This work introduces a sequential convex programming framework for non-linear, finitedimensional stochastic optimal control, where uncertainties are modeled by multidimensional Wiener process. We prove that any accumulation point of the sequence iterates generated is candidate locally-optimal solution original problem in sense Pontryagin Maximum Principle. Moreover, we provide sufficient condit...