Heuristic algorithm for optimal redundancy allocation in complex systems: A 3-neighborhood approach

Abstract

Purpose

The purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.

Design/methodology/approach

The algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.

Findings

The proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.

Research limitations/implications

In general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.

Practical implications

It is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.

Social implications

Reliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.

Originality/value

The utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

Citation

Aggarwal, S. (2019), "Heuristic algorithm for optimal redundancy allocation in complex systems: A 3-neighborhood approach", Journal of Quality in Maintenance Engineering, Vol. 25 No. 1, pp. 54-64. https://doi.org/10.1108/JQME-01-2018-0003