We propose a new algorithm for the solution of the robust multiple-load topology optimization problem. The algorithm can be applied to any type of problem, e.g., truss topology, variable thickness sheet or free material optimization. We assume that the given loads are uncertain and can be subject to small random perturbations. Furthermore, we define a rigorous measure of robustness of the given...

We present a robust optimization approach to 0-1 linear programming with uncertain objective coefficients based on a safe tractable approximation of chance constraints, when only the first two moments and the support of the random parameters is known. We obtain nonlinear problems with only one additional (continuous) variable. The resulting robust optimization problems can be interpreted as a n...

This paper considers the problem of robust H filter design in uncertain discrete-time singular systems with possible missing measurements due to unreliable network transmission channels. The stochastic variable satisfying Bernoulli random binary distribution is introduced to model the missing phenomena and the corresponding filtering error dynamics with delay is then induced. We provide a set o...

This paper analyzes the relative performance of two market-based fisheries management instruments in the presence of a stochastic stock-recruitment relation. Regulators are forced to choose either a fee per fish landed or a quota on the total fish harvest at a time when they are uncertain what will be the stock of fish recruits that will actually materialize during the next fishing period. With...

This paper aims to determine the reliability of a complex system using Bayesian network. A network (BN) is probabilistic graphical model that represents knowledge about an uncertain domain where each component corresponds random variable and edge corresponding conditional probability. used estimate multistate consecutive k-out-of-n: F reliability. presents construction proposed system. The line...

A fuzzy random variable (FRV) has been conceptualized as a vague perception of a crisp but unobservable random variable (RV) and also as a random fuzzy set. Since actuaries seem receptive to these notions of FRV, and there are sources of uncertainty that RVs can not accommodate, but FRVs can, one would expect FRVs to be a component of the actuarial arsenal, but generally this is not the case. A...

One of the main topic in the development of predictive models is the identification of variables which are predictors of a given outcome. Automated model selection methods, such as backward or forward stepwise regression, are classical solutions to this problem, but are generally based on strong assumptions about the functional form of the model or the distribution of residuals. In this paper a...