This is a pioneer study that presents two branches of computational intelligence techniques, namely linear genetic programming (LGP) and radial basis function (RBF) neural network to build models for bankruptcy prediction. The main goal is to classify samples of 140 bankrupt and non-bankrupt Iranian corporations by means LGP and RBF. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection analysis. In order to benchmark the proposed models, a log-log regression analysis is further performed. A comparative study on the classification accuracy of the LGP, RBF and regression-based models is conducted. The results indicate that the proposed models effectively let estimate any enterprise in the aspect of bankruptcy. The LGP models have a significantly better prediction performance in comparison with the RBF and regression models.

This article deals with a new redundancy allocation model for non-repairable series-parallel systems with multiple strategy choices. The proposed model simultaneously determines the type of components, number of active and standby components to maximize system reliability subject to design constraints. Traditionally, due to complexity and difficulty in obtaining the closed form version of system reliability, a convenient lower-bound on system reliability has been widely applied to approximate it. Assuming that switching mechanism time-to-failure is exponentially distributed, the closed form version of the reliability of subsystems with cold standby redundancy is derived analytically for the first time. This is successfully performed using Markov process and solving the relevant set of differential-difference equations. With respect to the obtained formulation, a semi-analytical expression for the reliability of subsystems with mixed redundancy strategy is also extracted. Component time-to-failure is assumed to follow an Erlang distribution which is suitable for most engineering design problems. The presented model is linear and in the form of standard zero-one integer programs and thus using integer programming algorithms guarantees optimal solutions. The computational results of solving a well-known example indicate the high performance of the proposed model in improving system reliability.

The redundancy allocation problem (RAP) of non-repairable series-parallel systems considering cold standby components and imperfect switching mechanism has been traditionally formulated with the objective of maximizing a lower bound on system reliability instead of exact system reliability. This objective function has been considered due to the difficulty of determining a closed-form expression for the system reliability equation. But, the solution that maximizes the lower bound for system reliability does not necessarily maximize exact system reliability and thus, the obtained system reliability may be far from the optimal reliability. This article attempts to overcome the mentioned drawback. Under the assumption that component time-to-failure is distributed according to an Erlang distribution and switch time-to-failure is exponentially distributed, a closed-form expression for the subsystem cold standby reliability equation is derived by solving an integrodifference equation. A semi-analytical expression is also derived for the reliability equation of a subsystem with mixed redundancy strategy. The accuracy and the correctness of the derived equations are validated analytically. Using these equations, the RAP of non-repairable series-parallel systems with a choice of redundancy strategies is formulated. The proposed mathematical model maximizes exact system reliability at mission time given system design constraints. Unlike most of the previous formulations, the possibility of using heterogeneous components in each subsystem is provided so that the active components can be of one type and the standby ones of the other. The results of an illustrative example demonstrate the high performance of the proposed model in determining optimal design configuration and increasing system reliability.

Abstract. This paper studies a warm standby repairable system including two dissimilar units: one repairman and imperfect switching mechanism. Times to failure and times to repair of active and standby units are assumed to be exponentially distributed. Two cases of unreliable switching mechanism are considered. In case one, the failed active unit will be replaced by the available warm standby unit with coverage probability c. However, in case two, the switching mechanism is repairable, and its failure time and repair time are also exponentially distributed. Using Markov process and Laplace transforms, the explicit expressions of the mean time to failure, MTTF, and the steady-state availability of the two systems are derived analytically. Finally, by solving a numerical example, the two systems are compared based on various reliability and availability characteristics. Moreover, sensitivity analyses of the reliability and availability indexes are accomplished with respect to the model parameters.