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Multi-objective Redundancy Allocation Problem with weighted-k-out-of-n subsystems.

Mani Sharifi1, Tahmine Ashoori Moghaddam2, Mohammadreza Shahriari3

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Summary

This study introduces a new model for the Redundancy Allocation Problem (RAP) involving weighted-k-out-of-n parallel systems. It optimizes reliability and cost using genetic algorithms and universal generating functions.

Keywords:
Non-dominated ranked genetic algorithm (NRGA)Non-dominated sorting genetic algorithms (NSGA-II)ReliabilitySystems engineeringWeighted-k-out-of-n

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Area of Science:

  • Operations Research
  • Reliability Engineering
  • System Optimization

Background:

  • The Redundancy Allocation Problem (RAP) is a critical area in reliability engineering.
  • Recent advancements aim to enhance RAP models for real-world applicability, including weighted-k-out-of-n subsystems.
  • These subsystems are vital for modeling complex systems like power and hydro transitions.

Purpose of the Study:

  • To develop a novel multi-objective RAP (MORAP) model.
  • To optimize both reliability and cost for weighted-k-out-of-n parallel systems.
  • To adapt the universal generating function for exact sub-system reliability calculation.

Main Methods:

  • Formulating a multi-objective RAP model for weighted-k-out-of-n parallel systems.
  • Utilizing the universal generating function to derive an exact reliability formula for subsystems.
  • Employing non-dominated sorting genetic algorithm (NSGA) and non-dominated ranked genetic algorithm (NRGA) due to the NP-hard nature of RAP.

Main Results:

  • The study successfully adapted the universal generating function for precise sub-system reliability calculation.
  • Both NSGA and NRGA were applied to solve the MORAP.
  • Performance comparison of NSGA and NRGA was conducted using several criteria.

Conclusions:

  • The proposed MORAP model effectively addresses the optimization of reliability and cost in weighted-k-out-of-n parallel systems.
  • The application of genetic algorithms provides efficient solutions for this complex optimization problem.
  • The study contributes a robust framework for reliability and cost optimization in advanced system designs.