RESEARCH PAPER
Redundancy Allocation Optimization for k-out-of-n:G Systems Considering Imperfect Switching and Uncertainty
1
Nanchang University, China
2
Macau University of science and technology, Macau
Submission date: 2026-01-15
Final revision date: 2026-03-09
Acceptance date: 2026-03-27
Online publication date: 2026-04-02
KEYWORDS
Monte Carlo simulationFuzzy numbersGenetic algorithmRedundancy allocation strategyGeneralized stochastic Petri nets
TOPICS
ABSTRACT
In safety-critical fields like energy and aerospace, optimizing system reliability is essential. This paper presents a novel Redundancy Allocation Problem (RAP) approach under component lifetime parameter uncertainties. By integrating Generalized Stochastic Petri Nets (GSPN) with Monte Carlo Simulation (MCS), a reliability evaluation framework is developed for k-out-of-n: G systems with imperfect switching, supporting active, cold standby, and mixed redundancy strategies. To jointly optimize component types, redundancy quantities, and strategies, a Genetic Algorithm with fixed-random-seed fitness evaluation eliminates Monte Carlo sampling variance, maximizing system reliability subject to cost and weight constraints. The proposed framework is validated through a case study on a natural gas compressor pipeline system. This work provides a quantitative decision-support tool enabling simultaneous selection of component types, redundancy levels, and strategies while characterizing solution reliability through confidence intervals under parameter uncertainty.
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| ISSN: | 1507-2711 |
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