Availability Improvement Method for Repairable Systems Using Modified Ant Colony Optimisation

Reliability is deemed as an important issue in many broad applications, e.g., telecommunication systems, electric power systems, computation and parallel processing systems. In order to reach efficient performance and reliable/available design, we need to optimize the cost and reliability/availability of the proposed designs. The main contribution of this paper is to address the optimum model for repairable component with series-parallel structure using redundancy allocation problem (RAP). In this regard, two novel modified Ant Colony Optimization (ACO), are defined. In order to improve availability, ACOs employ visibility and pheromone update to optimize the RAP. The proposed methodology includes single objective to maximize the availability of case study system with two constraints-weight and cost of components. Hence, three meta-heuristic algorithms, i.e., ACO, Genetic algorithm (GA) and Particle Swarm Optimization (PSO) are applied to find the optimum structure. Finally, the simulation results of all the proposed meta-heuristic algorithms are compared. The comparison reveals that the modified ACO (MACO) provides the maximum availability among all the other meta-heuristic algorithms.