The paper considers estimating a parameter β that defines an estimating function U.y,x,β/ for an outcome variable y and its covariate x when the outcome is missing in some of the observations.We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying t...

Real-world data often includes rich relational information, which can be leveraged to help predict unknown variables using a small amount of observed variables via a propagation effect. We consider the problem of selecting the best subset of variables to observe to maximize the overall prediction accuracy. Under the Bayesian framework, the optimal subset should be chosen to minimize the Bayesia...

In this paper, we employ the reduced basis method as a surrogate model for the solution of linear-quadratic optimal control problems governed by parametrized elliptic partial differential equations. We present a posteriori error estimation and dual procedures that provide rigorous bounds for the error in several quantities of interest: the optimal control, the cost functional, and general linea...

Simulation is widely used in the decision making processes associated with supply chain management. In this paper, we present an extension of the simulation based optimization framework which has been previously proposed for analyzing supply chains. The extension consists of the iterative construction of a surrogate model based on systematically accumulated simulation results to capture the cau...

This paper discussed possible application of soft computing methods in uncertainty management of the HR component in steel industry during automation period. It explained the importance of HR and the concept of uncertainty management. Then it discussed what to look in soft computing for uncertainty management. Keywords-HR, Soft Computing, space mapping, surrogate model, uncertainty management

We consider parameter optimization problems which are subject to constraints given by parametrized partial differential equations (PDE). Discretizing this problem may lead to a largescale optimization problem which can hardly be solved rapidly. In order to accelerate the process of parameter optimization we will use a reduced basis surrogate model for numerical optimization. For many optimizati...

To analyze the effects of specific regions of the aleatory and epistemic uncertain variables on the failure probability, a regional sensitivity analysis (RSA) technique called contribution to failure probability (CFP) plot is developed in this paper. This RSA technique can detect the important aleatory and epistemic uncertain variables, and also measure the contribution of specific regions of t...

An employee transporting problem is described and a set partitioning model is developed. An investigation of the model leads to a knapsack problem as a surrogate problem. Finding a partition corresponding to the knapsack problem provides a solution to the problem. An exact algorithm is proposed to obtain a partition (subset-vehicle combination) corresponding to the knapsack solution. It require...