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An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications

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  • Coelho, Leandro dos Santos

Abstract

The reliability–redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability–redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability–redundancy optimization problems. In this context, two examples in reliability–redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability–redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.

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  • Coelho, Leandro dos Santos, 2009. "An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 830-837.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:4:p:830-837
    DOI: 10.1016/j.ress.2008.09.001
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    2. Mohammad N Juybari & Mostafa Abouei Ardakan & Hamed Davari-Ardakani, 2019. "A penalty-guided fractal search algorithm for reliability–redundancy allocation problems with cold-standby strategy," Journal of Risk and Reliability, , vol. 233(5), pages 775-790, October.
    3. Mohammad N. Juybari & Pardis Pourkarim Guilani & Mostafa Abouei Ardakan, 2022. "Bi-objective sequence optimization in reliability problems with a matrix-analytic approach," Annals of Operations Research, Springer, vol. 312(1), pages 275-304, May.
    4. Attar, Ahmad & Raissi, Sadigh & Khalili-Damghani, Kaveh, 2017. "A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 177-191.
    5. Parhizkar, Tarannom & Vinnem, Jan Erik & Utne, Ingrid Bouwer & Mosleh, Ali, 2021. "Supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Part 1: General Overview," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Zhiming Shi & Yisong Li & Gábor Bohács & Qiang Zhou, 2022. "A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    7. Kim, Heungseob & Kim, Pansoo, 2017. "Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 153-160.
    8. Dobani, Ehsan Ramezani & Ardakan, Mostafa Abouei & Davari-Ardakani, Hamed & Juybari, Mohammad N., 2019. "RRAP-CM: A new reliability-redundancy allocation problem with heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Parhizkar, Tarannom & Utne, Ingrid Bouwer & Vinnem, Jan Erik & Mosleh, Ali, 2021. "Supervised dynamic probabilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

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