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<p>System design involves many challenges, especially in case of failure dependencies of the subsystems. Most design parameters can be determined using mathematical models. However, some parameters may be fuzzy. Fuzziness increases the criticality of the study and efficient methods should be used. This work addresses the system cost under availability constraints at the design stage of the series parallel-system under failure dependencies with fuzzy costs. Weak, linear, and strong failure dependencies are considered and the fuzzy costs are converted into crisp values using the ranking function method. The design variables are the allocations of the redundancy and repair teams at each subsystem. The optimization problem is solved through particle swarm optimization (PSO), grey wolf optimizer (GWO), and the constraints are handled using penalty functions. The results are compared by considering three scenarios of availability limits (0.90, 0.95, and 0.99). The study contributes to life-cycle reliability and safety engineering by providing a design-stage optimization framework that integrates uncertainty in cost parameters, enabling more realistic and robust system configurations that affect long-term operational availability and maintenance planning.</p>

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Fuzzy system design optimization under failure dependencies using defuzzification and nature-inspired techniques

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Houssam Eddine Semmar,
  • Mohamed Arezki Mellal

摘要

System design involves many challenges, especially in case of failure dependencies of the subsystems. Most design parameters can be determined using mathematical models. However, some parameters may be fuzzy. Fuzziness increases the criticality of the study and efficient methods should be used. This work addresses the system cost under availability constraints at the design stage of the series parallel-system under failure dependencies with fuzzy costs. Weak, linear, and strong failure dependencies are considered and the fuzzy costs are converted into crisp values using the ranking function method. The design variables are the allocations of the redundancy and repair teams at each subsystem. The optimization problem is solved through particle swarm optimization (PSO), grey wolf optimizer (GWO), and the constraints are handled using penalty functions. The results are compared by considering three scenarios of availability limits (0.90, 0.95, and 0.99). The study contributes to life-cycle reliability and safety engineering by providing a design-stage optimization framework that integrates uncertainty in cost parameters, enabling more realistic and robust system configurations that affect long-term operational availability and maintenance planning.