Reliability is one of the most important characteristics of the electrical and mechanical systems with applications in the space communication industries, internet networks, telecommunication systems, power generation systems, and productive facilities. What adds to the importance of reliability in these systems are system complications, nature of competitive markets, and increasing production costs due to failures. This paper investigates availability optimization of a system using both repairable and non-repairable components, simultaneously. The availability-redundancy allocation problems involve the determination of component availability (i.e., life time and repair time of the components) and the redundancy levels that produce maximum system availability. These problems are often subject to some constraints on their components such as cost, weight, and volume. To maximize the availability and to minimize the total cost of the system, a new Mixed Integer Nonlinear Programming (MINLP) model is presented. To solve the proposed model, an improved version of the genetic algorithm is designed as an efficient meta-heuristic algorithm. Finally, in order to verify the efficiency of the proposed algorithm, a numerical example of a system is presented that consists of both repairable and non-repairable components.

Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.