A hybrid decision method for enhancing resilience of complex systems integrating quantitative resilience evaluation and cost trade-offs

High-impact disturbances can easily cause the performance of complex systems to drop sharply or even fail, thereby leading to potential safety risks, a large amount of maintenance costs and losses. Investing in enhancing the resilience of complex systems is crucial for mitigating the adverse consequences caused by high-impact disturbance events and ensuring system operational safety. However, in practical engineering decision-making, it remains challenging to identify effective schemes from numerous potential improvement measures and quantitatively balance resilience benefits against implementation costs. Accordingly, this study proposes a hybrid decision method integrating resilience quantification and cost trade-offs to evaluate resilience enhancement schemes and identify a compromise solution. First, failure analysis is performed to derive corresponding resilience enhancement measures. Then, a Bayesian network (BN) is used to perform a static capability-based quantitative evaluation of different resilience enhancement schemes. Finally, the Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to generate the cost–resilience Pareto front, and the Knee Point method is applied to identify a compromise solution. Application to a complex-terrain drilling rig yields a baseline resilience score of 0.3089. S 9 is identified as the Knee Point of the unconstrained Pareto front, with a cost of 19.11 × 10 4 CNY and a resilience score of 0.5718, while under the additional M 8 -requirement, S 21 is identified as the Knee Point, with a cost of 41.63 × 10 4 CNY and a resilience score of 0.6545. These results demonstrate the capability of the proposed method to identify cost–resilience compromise solutions under different decision requirements.