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ABSTRACT Spare parts configuration involves various resource constraints (such as cost, mass, and volume) that affect equipment mission reliability. The traditional marginal effect method struggles to handle multi‐constraint coupling, and criticality analysis is typically performed separately from the optimization process. In this paper, we propose a general framework that integrates criticality analysis into multi‐constraint optimization. First, we construct a criticality‐weighted system availability objective function, where the weights are derived from a robust analytic hierarchy process (AHP) assessment model combined with Monte Carlo (MC) simulation. Second, we design a grid‐search‐based marginal optimization algorithm (G‐MOA). Which generalizes the marginal effect method for multiple constraints by systematically sampling the weight space via parallel optimizations? Case study results demonstrate that G‐MOA outperforms benchmark methods, achieving a 0.15%–0.47% improvement in system availability. Moreover, criticality weighting effectively directs resources toward critical spare parts, significantly reducing their shortage risk—with at most a 0.3% loss in system availability. This work provides an explicit, quantitative decision‐making tool for differentiated spare part support.
Mei et al. (Tue,) studied this question.