Mission Aborting inn-Unit Systems With Work Sharing
研究了不可修工作共享系统在主要任务中基于条件的任务中止规则，通过概率模型和遗传算法优化中止阈值以最小化损失期望，适用于化工反应器等系统。
Mission aborting has recently attracted great attention, where mission abort rules (MARs) have been modeled and optimized for different types of technological systems aiming to effectively mitigate the risk of system losses. However, none of the existing works have considered systems with multiple work-sharing units. This article makes advancements in the state of the art by modeling condition-based MARs for nonrepairable work-sharing systems that must perform a specified amount of work during the primary mission (PM). The MAR considered presumes aborting the PM to prevent considerable damage when the number of available units reduces to a certain number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> while the amount of work accomplished in the PM is less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> ). After the PM abortion, a rescue procedure (RP) is executed by the remaining units to survive the system. Dynamic operating conditions during PM and RP are considered. A probabilistic model-based numerical algorithm is proposed to evaluate several performance metrics, including mission success probability, RP success probability, damage avoidance probability, and expected cost of losses. The MAR optimization problem for minimizing the expected cost of losses is formulated and solved using the genetic algorithm. An example of a chemical reactor system is provided to demonstrate the proposed algorithm as well as the benefit of MARs optimized in comparison to the “no abort” policy and the most conservative abort policy (the PM aborts upon the failure of any unit). Effects of several model parameters on the system performance metrics and optimization solutions are also examined through examples.