Abstract
•Multi-attempt missions performed by multiple elements are considered.•Two groups of elements can obey different abort policies.•An algorithm for evaluating expected losses associated with the mission is suggested.•Optimal abort policies and elements distribution between the groups are found.
Modeling and optimizing mission abort policies have received intensive research attention in the past decade. While most of the existing models assumed single-attempt missions, few of them considered multi-attempt missions (i.e., a system component may re-attempt the mission after being rescued successfully and properly maintained). However, the existing multi-attempt studies assumed the same abort policy (AP) for all the system components. This paper extends the state of the art by putting forward a new shock-based AP model where a subset of available components during each attempt is identified as kamikaze components that operate with a riskier AP than the rest of the available components, and the number of kamikaze components and APs for both groups of components (kamikaze and non-kamikaze) may change from attempt to attempt. The AP optimization problem is solved using the genetic algorithm to minimize the total expected cost of losses. The problem is further generalized to incorporate the total number of components and the maximum number of mission attempts as additional decision variables. The proposed AP and solutions to both optimization problems are illustrated through a detailed case study of a multi-UAV system undergoing random shocks during both the primary surveillance mission and the rescue procedure.