A Decision Support System for Solving Multi-Objective Redundancy Allocation Problems

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series-parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System (DSS) for solving Multi-Objective RAPs (MORAPs). Initially, we use the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε–constraint method to generate nondominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis (DEA) model to prune the non-dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms.