System Engineering and Productivity

System Engineering and Productivity

Solving the Reliability-Surplus Allocation Problem Using a Hybrid Colonial Competitive and Genetic Algorithm

Document Type : Research Paper

Authors
Azadeh Andarkhor 1
Hossein Eghbali 2
1 M.Sc., Department of Industrial Engineering, Faculty of Industrial Engineering, University of Eyvanekey, Eyvanekey, Iran
2 Corresponding author: Assistant Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, University of Eyvanekey, Eyvanekey, Iran
10.22034/sep.2022.243415
Abstract
Reliability is an important characteristic in electrical and mechanical systems. Finding the optimal level of reliability based on the constraints in the system is called the reliability optimization problem. One of the methods to improve the reliability of the system is to use redundant components in the system. Assigning appropriate reliability to each component and using redundant components in parallel with the main components of the system is known as reliability-redundancy allocation. It has been proven that the redundancy allocation problem is a non-deterministic polynomial optimization problem and the computations increase exponentially with increasing problem size and constraints. Therefore, finding a suitable solution to this class of problems is important. In this research, the redundancy-redundancy allocation problem was investigated in improving the reliability of series, series-parallel and bridge systems, and a combination of colonialist and genetic algorithms was used to solve the problems. The proposed algorithm has shown better performance compared to previous approaches. In fact, the present study has presented an appropriate algorithm and solution method for solving the reliability optimization problem.
Keywords
Reliability improvement
Redundancy-surplus allocation
Imperialist competitive algorithm
Hybrid algorithm

Copyright ©, Azadeh Andarkhor, Hossein Eghbali

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Garg, H., Rani, H., Sharma, S., & Vishwakarma, Y. (2014). Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment. Expert Syst Appl, 57-67
Zoulfagharia, H., Zeinal Hamadania, A., & Abouei Ardakanb, M. (2015). Multi- objective availability-redundancy allocation problem for a system with repairable and non-repairable components. Decision Science Letters, 289-302.
Chern, M. (1992). On the Computational Complexity of Reliability Redundancy Allocation in a Series System. Operations Research Letters, 11, 309-315.
Yalaoui, A., chatelet, E., & Chu, C. (2005). A new dynamic programming method for reliability_redundancy allocation in a parallel-series system. IEEE Transactions on Reliability, 2(54), 254-261.
.Billionnet, A. (2008). Redundancy allocation for series-parallel systems using integer linear programming. IEEE Transactions on Reliability(57), 507-16.
Ha, C., & Kuo, W. (2006). Reliability redundancy allocation:An improved realization for nonconvex nonlinear programming problems. European Journal of Operational Research, 1(127), 24-38.
Ramirez-Marquez, J. E., Coit, D. W., & Konak, A. (2004). Redundancy allocation for series-parallel systems using a max-min approach. Iie Transactions, 36(9), 891-898
Lins, I., & Droguett, E. (2011). Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation. Simul. Model. Pract. Theory(19), 362-281.
Ardakan, M. A., & Hamadani, A. Z. (2014)Reliability optimization of series–parallel systems with mixed redundancy strategy in subsystems. Reliability Engineering & System Safety, 130, 132-139.
Garg, H., & Sharma, S. (2013). Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Comput Ind Eng, 64(1), 247-355.
Zou, D., Gao, L., Li, S., & Wu, J. (2011). An effective global harmony search algorithm for reliability problems. Expert Syst ApP, 4644-4648.
Garg, H. (2015). An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm. Beni-Suef University Journal of Basic and Applied Sciences, 4(1), 14-25.
Sharifi, M., & Mombeini, H. (2015). The effects of technical and organizational activities on redundancy allocation problem with choice of selecting redundancy strategies using imperialist competition algorithm. The Business& Management Review, 6(2).
Khorshidi, H. A., Gunawan, I., & Ibrahim, M. Y. (2015). On reliability evaluation of multistate weighted k-out-of-n system using present value. The Engineering Economist, 60(1), 22-39.
Levitin, G., Xing, L., & Dai, Y. (2014). Cold vs. hot standby mission operation cost minimization for 1-out-of-N systems. European Journal of Operational Research, 234(1), 155-162
Wang, Y., & CAI, Z. (2009). A hybrid multi-swarm particle swarm optimization to Solve constrained optimization problems. Frontiers of Computer Science in China, 3(1), 52-38
Heiling, L., & Vob, S. (2014). A scientometric analysis of redundancy allocation literature(1969–2013):Technical report. Institute of information Systems,University of Hamburg.
Sheikhalishahi, M., Ebrahimipour, V., & Shiri, H. (2013). A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. Int J Adv Manuf Technol, 317-338.
Moghadam, M. R., Afsar, a., & Sohrabi, b. (1384). Modeling of supply chain with genetic algorithm approach. (In Persian)
Valente, J., & Goncalves, J. (2009). A genetic algorithm approach for the singlemachine scheduling prolem with linear earliness and quadratic tradiness penalties. Computers and Operations Engineering, 36, 2707-2715.
.Liu, Y. L. (2002). Reactive power optimization by GA/SA/TS combined algorithms. International Journal of Electrical Power & Energy Systems,2002., 24(9), 765-769.
Khorani, A. V., Farzad Razavi, B., & Ahsan Ghoncheh, C. (2010). A new hybrid evolutionary algorithm based on ICA and GA: Recursive-ICA-GA. The International Conference on Artificial Intelligence.
System Engineering and Productivity
Volume 2, Issue 3 - Serial Number 4
Serial No. 4, Autumn Quarterly
Autumn 2022
Pages 27-47

  • Receive Date 26 September 2022
  • Revise Date 11 October 2022
  • Accept Date 14 October 2022
  • First Publish Date 14 October 2022
  • Publish Date 22 November 2022