Fuzzy reliability redundancy optimisation with signed distance method for defuzzification using genetic algorithm

Sanat Kumar Mahato, Nabaranjan Bhattacharyee and Rajesh Pramanik

International Journal of Operational Research, 2020, vol. 37, issue 3, 307-323

Abstract: Consideration of impreciseness is more realistic for modelling of physical phenomena. This impreciseness can be considered in several ways like, interval/stochastic/fuzzy or mixture of these. In this work, we have taken for optimising of the system reliability of a redundancy allocation problem formulated from a complex network system with imprecise parameters in the form of trapezoidal fuzzy numbers (TrFN). The signed distance method has been used to defuzzify the fuzzy values. Then big-M penalty technique is used to transform the problem to unconstrained optimisation problem. To solve these problems, we have implemented the real coded elitist genetic algorithm (RCEGA) for integer variables with tournament selection, intermediate crossover and one neighbourhood mutation. For illustration, the five link bridge network system has been solved and the results have been presented.

Keywords: reliability-redundancy allocation; imprecise environment; genetic algorithm; fuzzy number; defuzzification; signed distance method; SDM; penalty function. (search for similar items in EconPapers)
Date: 2020
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