Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment generation transmission capacity. We build on this setting by considering a multi-year horizon for finding the optimal plan capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours power-syste...

Abstract We study a family of discrete optimization problems asking for the maximization expected value concave, strictly increasing, and differentiable function composed with set-union operator. The is computed respect to set coefficients taking values from scenarios. models utility decision maker, while operator covering relationship between two ground sets, items metaitems. This problem gene...

Abs t r ac t . J . F . Benders devised a clever approach for exploiting the structure of mathematical programming problems with complicating variables (variables which, when temporarily fixed, render the remaining optimization problem considerably more tractable). For the class of problems specifically considered by Benders, fixing the values of the complicating variables reduces the given prob...

This paper presents an application of Benders decomposition to deal with the complexities in the simultaneous Generation Expansion Planning (GEP) and Transmission Expansion Planning (TEP). Unlike the power system operation fields of study, the power system planning methods are not expected to be fast. However, it is always preferable to speed up computations to provide more analysis options for...

Multistage stochastic linear programming has many practical applications for problems whose current decisions have to be made under future uncertainty. There are a variety of methods for solving the deterministic equivalent forms of these dynamic problems, including the simplex and interior point methods and nested Benders decomposition { which decomposes the original problem into a set of smal...

In today’s competitive business environment, the design and management of supply chainnetwork is one of the most important challenges that managers encounter. The supply chain network shouldbe designed for satisfying of customer demands as well as minizing the total system costs. This paper presentsa multi-period multi-stage supply chain network design problem under demand uncertainty. The prob...

We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear reco...

We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear reco...

This paper presents numerical approximation schemes for a two stage stochastic programming problem where the second stage problem has a general nonlinear complementarity constraint: first, the complementarity constraint is approximated by a parameterized system of inequalities with a wellknown regularization approach [44] in deterministic mathematical programs with equilibrium constraints; the ...