A Stochastic Approach for Evaluating the Reliability of a MASS and Assessing the Compliance with the IMO Regulatory Framework

The introduction of Maritime Autonomous Surface Ships (MASSs) requires reliable engineering methods to demonstrate safety levels comparable to those of conventional manned vessels. This paper presents a mission-based stochastic reliability framework for the comparative assessment of manned and unmanned ship configurations, with a focus on engineering design and verification. The methodology is based on Reliability Block Diagrams (RBDs), where ship missions are decomposed into critical functional subsystems and evaluated over mission-limited operational profiles. To address the uncertainty inherent in component failure data for autonomous systems, Mean Time to Failure (MTTF) values are treated as stochastic variables rather than fixed parameters. A Monte Carlo simulation approach is used to propagate uncertainty from component level to overall mission reliability, producing probability distributions of mission success. The proposed framework is applied to a Search and Rescue Patrol Vessel, comparing manned and unmanned configurations under identical mission scenarios and durations. Results indicate that, for moderate uncertainty levels, the unmanned configuration achieves equal or higher mission reliability, while increasing uncertainty reduces the statistical separation between the two solutions. The approach provides a practical and replicable tool to support reliability-driven design decisions for autonomous marine systems.