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

Author

Listed:
  • Sanat Kumar Mahato
  • Nabaranjan Bhattacharyee
  • Rajesh Pramanik

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.

Suggested Citation

  • Sanat Kumar Mahato & Nabaranjan Bhattacharyee & Rajesh Pramanik, 2020. "Fuzzy reliability redundancy optimisation with signed distance method for defuzzification using genetic algorithm," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 37(3), pages 307-323.
    • Handle: RePEc:ids:ijores:v:37:y:2020:i:3:p:307-323
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    Cited by:

    1. Rajesh Paramanik & Nirmal Kumar & Sanat Kumar Mahato, 2022. "Solution for the optimality of an intuitionistic fuzzy redundancy allocation problem for complex system using Yager’s ranking method of defuzzification with soft computation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 615-624, April.

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