Artificial neural networks for reliability maximization under budget and weight constraints

Abstract

Purpose

The purpose of this paper is to apply a recent kind of neural networks in a reliability optimization problem for a series system with multiple‐choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget and weight. The problem is formulated as a non‐linear binary integer programming problem and characterized as an NP‐hard problem.

Design/methodology/approach

The design of neural network to solve this problem efficiently is based on a quantized Hopfield network (QHN). It has been found that this network allows one to obtain optimal design solutions very frequently and much more quickly than other Hopfield networks.

Research limitations/implications

For systems more complex than series systems considered in this paper, the proposed approach needs to be adapted. The QHN‐based solution approach can be applied in many industrial systems where reliability is considered as an important design measure, e.g. in manufacturing systems, telecommunication systems and power systems.

Originality/value

The paper develops a new efficient method for reliability optimization. The most interesting characteristic of this method is related to its high‐speed computation, since the practical importance lies in the short computation time needed to obtain an optimal or nearly optimal solution for large industrial problems.

Citation

Nourelfath, M. and Nahas, N. (2005), "Artificial neural networks for reliability maximization under budget and weight constraints", Journal of Quality in Maintenance Engineering, Vol. 11 No. 2, pp. 139-151. https://doi.org/10.1108/13552510510601339