We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These are prevalent science, governance and industry. The first method we is generic decomposes constraints into a multitude smaller local that solved using programming (CP) or mixed-integer (MIP) solver. However, many SCPs formulated on probability distribution...

In this paper we have investigated the performance of some stochastic methods for solving constraint satisfaction (CSP) and fuzzy constraint satisfaction problems (FCSP). The purpose of this paper is to study the abilities of learning automaton in solving these problems and comparing it with other stochastic methods. The results confirm those of [2] and show its superiority to other methods.

To model combinatorial decision problems involving uncertainty and probability, we have proposed “stochastic constraint programming” [3]. This extends constraint programming with stochastic variables, chance constraints and optimized expectations. We propose extending the OPL modelling language [1] with these features, and show how they can be compiled away using some simple rules.

We investigate risk-averse stochastic optimization problems where riskaverse preferences are modeled with a stochastic order constraint. We propose augmented Lagrangian methods for the numerical solution of problems with multivariate and univariate stochastic order relations. The methods constructs finite-dimensional approximations of the optimization problem whose solutions converge to the sol...