The method for exactly solving bi-objective RAP with phase-type distribution under mixed redundancy strategy

Jiangang Li, Dan Wang, Haoxiang Yang and Shubin Si

Reliability Engineering and System Safety, 2026, vol. 265, issue PB

Abstract: High reliability is essential for complex industrial systems. Redundancy design enhances system reliability, making the redundancy allocation problem (RAP) pivotal in reliability optimization. However, existing bi-objective RAP models under a mixed redundancy strategy rely on approximate reliability calculations or restrictive assumptions of exponential time-to-failure (TTF) distributions for components. Furthermore, heuristic-based approaches currently adopted for solving bi-objective RAP neither guarantee Pareto optimal nor systematically identify all optimal solutions. This study proposes a novel bi-objective RAP model with exact reliability evaluation, where the TTF distribution of components is considered a generalized phase-type distribution. For the first time, an exact dynamic programming (DP) algorithm is developed to solve bi-objective RAP models under a mixed redundancy strategy, ensuring the identification of complete Pareto-optimal solutions. Numerical experiments demonstrate that our work, with an accurate reliability evaluation method and an exact DP algorithm, achieves a higher-quality Pareto frontier with superior reliability and lower costs than previous studies. Additionally, the algorithm is used to obtain the exact solution of single-objective RAP, further expanding our research’s theoretical depth and application scope in reliability optimization. Finally, a case study about a supervisory control and data acquisition system confirms the method’s operational effectiveness and applicability in reliability engineering.

Keywords: Bi-objective redundancy allocation problem; Exact algorithm; Mixed redundancy strategy; Heterogeneous components; Phase-type distribution; Continuous-Time Markov Chain (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:265:y:2026:i:pb:s0951832025007987

DOI: 10.1016/j.ress.2025.111598

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