Cluster system maintenance scheduling multi-objective optimization method integrating time uncertainty GlueVaR risk

Uncertainty in maintenance timing affects planning for systems with time window constraints, creating risks of overflow and operational disruptions. This paper proposes a multi-objective robust optimization method for Cluster system maintenance planning, integrating Glue Value at Risk (GlueVaR) to capture timing uncertainty. The method restores system reliability through maintenance while using GlueVaR to quantify timing uncertainty. Using GlueVaR's multi-parameter features to capture decision-makers' risk preferences, the method embeds maintenance strategies and decision tendencies into system metrics. The approach constructs a multi-objective optimization model with nested maintenance-level decisions and task scheduling. An improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) solves the model, screens optimal solutions, and analyzes time window overflow risk. Simulations on equipment clusters from outdoor signaling systems at railway stations show that maintenance risks decrease by 31.13 %, 45.54 %, and 61.09 % under generally optimistic, relatively conservative, and conservative decision-making tendencies, respectively. These results confirm the correctness and effectiveness of the proposed methodology.