Type-2 fuzzy reliability redundancy allocation problem and its solution using particle swarm optimization algorithm

• URL: http://arxiv.org/abs/2005.00863v1
• Date: Sat, 2 May 2020 15:39:54 GMT
• Title: Type-2 fuzzy reliability redundancy allocation problem and its solution using particle swarm optimization algorithm
• Authors: Zubair Ashraf, Pranab K. Muhuri, Q. M. Danish Lohani, and Mukul L. Roy
• Abstract summary: The fuzzy multi-objective reliability redundancy allocation problem (FMORRAP) is proposed. FMORRAP is proposed, which maximizes the system reliability while simultaneously minimizing the system cost.
• Score: 9.760638545828497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, the fuzzy multi-objective reliability redundancy allocation problem (FMORRAP) is proposed, which maximizes the system reliability while simultaneously minimizing the system cost under the type 2 fuzzy uncertainty. In the proposed formulation, the higher order uncertainties (such as parametric, manufacturing, environmental, and designers uncertainty) associated with the system are modeled with interval type 2 fuzzy sets (IT2 FS). The footprint of uncertainty of the interval type 2 membership functions (IT2 MFs) accommodates these uncertainties by capturing the multiple opinions from several system experts. We consider IT2 MFs to represent the subsystem reliability and cost, which are to be further aggregated using extension principle to evaluate the total system reliability and cost according to their configurations, i.e., series parallel and parallel series. We proposed a particle swarm optimization (PSO) based novel solution approach to solve the FMORRAP. To demonstrate the applicability of two formulations, namely, series parallel FMORRAP and parallel series FMORRAP, we performed experimental simulations on various numerical data sets. The decision makers/system experts assign different importance to the objectives (system reliability and cost), and these preferences are represented by sets of weights. The optimal results are obtained from our solution approach, and the Pareto optimal front is established using these different weight sets. The genetic algorithm (GA) was implemented to compare the results obtained from our proposed solution approach. A statistical analysis was conducted between PSO and GA, and it was found that the PSO based Pareto solution outperforms the GA.

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