Mission-based system emergency risk management via adaptive rescue decisions

Aborting a mission and initiating emergency rescue represent intuitive strategies for mitigating safety risks during mission execution. This study explores the management of emergency risks through adaptive rescue decisions. Our objective is to develop a dynamic decision-making approach for rescue that minimizes the expected costs associated with system malfunctions and mission failures, while also accounting for mission completion rewards. Most existing mission abort models presuppose a brand-new system prior to mission initiation and focus on formulating abort policies tailored to single-mission scenarios. By contrast, our work incorporates the system's initial age before mission execution and multiple sequential missions. To formalize this complex decision-making process, we conceptualize the sequential abort problem using a Markov decision process (MDP). Our findings reveal that optimal abort decisions rely on establishing control limits, which are derived from key parameters including the distributions of system lifetimes, mission durations, and rescue durations. Additionally, we identify specific conditions that dictate binary decisions: either continuing the mission under certain system states or aborting the mission across all possible states. We further provide sufficient conditions regarding the system's initial age prior to mission execution, which facilitate explicit differentiation between two scenarios: whether initiating the mission is optimal, and whether it is more advisable to avoid starting the mission. To demonstrate the practical relevance of our framework, we present detailed case studies focused on railway signal control systems.