under the smart grid environment, demand response resources (drrs) are introduced as a virtual power plant to enhance power system adequacy. drrs often fail to reduce their load due to some external factors. in this paper, a reliability model of a drr is constructed as multi-state conventional generation units, where the probability, frequency of occurrence, and departure rate of each state can...

optimization of maintenance resources to maximize the system availability is a major concern in different manufacturing systems. therefore, a lot of effort is put to construct optimization models to reach the maximum availability level and to reduce the costs of lack of availability. however, despite these efforts, data uncertainty in the real world problems was neglected in proposed models whi...

Rough data envelopment analysis (RDEA) evaluates the performance of the decision making units (DMUs) under rough uncertainty assumption. In this paper, new discussion regarding RDEA is extended. The RSBM model is proposed by integrating SBM model and rough set theory. The process of reaching solution is presented and this model is applied to efficiency evaluation of the DMUs with uncertain ...

for effective crisis management, appropriate telecommunications, which are required to fast response, are crucial. therefore, lack of existing proper telecommunications detract senior managers’ awareness about crisis conditions and also disturb rescuers in relief, causing increasing attendance time to the injured and fatalities. transfer point location problem is one of the new issues in the li...

The evolution of Bayesian approaches for model uncertainty over the past decade has been remarkable. Catalyzed by advances in methods and technology for posterior computation, the scope of these methods has widened substantially. Major thrusts of these developments have included new methods for semi-automatic prior specification and posterior exploration. To illustrate key aspects of this evolu...

In statistical exercises where there are several candidate models, the traditional approach is to select one model using some data driven criterion and use that model for estimation, testing and other purposes, ignoring the variability of the model selection process. We discuss some problems associated with this approach. An alternative scheme is to use a model-averaged estimator, that is, a we...