Placement of sensors in water distribution networks helps timely detection of contamination and reduces risk to the population. Identifying the optimal locations of these sensors is important from economic perspective and has been previously attempted using the theory of optimization. This work extends that formulation by considering uncertainty in the network and describes a stochastic program...

We propose a two-stage stochastic programming framework for designing or identifying “resilient”, or “repairable” structures in graphs whose topology may undergo a stochastic transformation. The repairability of a subgraph satisfying a given property is defined in terms of a budget constraint, which allows for a prescribed number of vertices to be added to or removed from the subgraph so as to ...

Mobile ad-hoc networks: a new stochastic second-order cone programming approach Serie Ricerca Mobile ad-hoc networks: a new stochastic second-order cone programming approach Abstract. We study the semidefinite stochastic location-aided routing (SLAR) model described in Ariyawansa and Zhu (2006) [2] and in Zhu, Zhang, and Patel (2007) [16]. We propose a modification of their model to exploit the...

Decomposition has proved to be one of the more effective tools for the solution of large-scale problems, especially those arising in stochastic programming. A decomposition method with wide applicability is Benders’ decomposition, and has been applied to both stochastic programming, as well as integer programming problems. However, this method of decomposition relies on convexity of the value f...

We survey structural properties of and algorithms for stochastic integer programming models, mainly considering linear two-stage models with mixedinteger recourse (and their multi-stage extensions).

For a long time modeling approaches to stochastic programming were dominated by scenario generation methods. Consequently the main computational effort went into development of decomposition type algorithms for solving constructed large scale (linear) optimization problems. A different point of view emerged recently where computational complexity of stochastic programming problems was investiga...

We give multi-stage stochastic programming formulations for lot-sizing problems where costs, demands and order lead times follow a general discrete-time stochastic process with finite support. We characterize the properties of an optimal solution and give a dynamic programming algorithm, polynomial in input size, when orders do not cross in time.

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...

Moment robust optimization models formulate a stochastic problem with an uncertain probability distribution of parameters described by its moments. In this paper we study a two-stage stochastic convex programming model using moments to define the probability ambiguity set for the objective function coefficients in both stages. A decomposition based algorithm is given. We show that this two-stag...