A bi-objective model to optimize reliability and cost of k-out-of-n series-parallel systems with tri-state components

Document Type : Article

Authors

1 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran

3 Department of Industrial Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9414, Iran

4 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

Redundancy Allocation Problem (RAP) is one way to increase system reliability. In most of the models developed so far for the RAP, system components are considered to have a binary state consisting of \working perfect" or \completely failed". However, to suit the real-world applications, this assumption has been relaxed
in this paper, such that components can have three states. Moreover, a Bi-Objective RAP (BORAP) is modeled for a system with serial subsystems, in which non-repairable tri-state components of each subsystem are con gured in parallel and the subsystem works under k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can a ect failure rates of the components, and hence can improve the system performance are also taken into account. The aim is to nd the optimum number of redundant components in each subsystem, such that the system reliability is maximized while the cost is minimized within some real-world constraints. In order to solve the complicated NP-hard problem at hand, the multi-objective Strength Pareto EvolutionaryAlgorithm (SPEA-II) is employed. As there is no benchmark available,  the Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to validate the results obtained. Finally, the
performances of the algorithms are analyzed using 20 test problems.

Keywords

Main Subjects

Scientia Iranica
Volume 24, Issue 3
Transactions on Industrial Engineering (E)
May and June 2017
Pages 1585-1602

Files

History

  • Receive Date: 27 April 2016
  • Revise Date: 15 January 2017
  • Accept Date: 28 January 2017

Share

How to cite

Statistics