Optimal scheduling, loading and aborting in additive missions performed by multiple components in shock environment

As an effective risk control method, mission aborting has recently received significant research attention. However, majority of the models failed to address the effects of loading on mission work progress and system loss risk. The very few models considering loading assumed single-attempt missions or single operating component. This work contributes by modeling the scheduling, loading and aborting policy (SLAP) for a multi-attempt mission system where multiple components are activated one by one according to a certain schedule (allowing overlapped operations) to accomplish a specified amount of work. The mission success depends on cumulative work accomplished by different components. We formulate and solve a new optimization problem that determines the SLAP to minimize the expected mission losses (EML) incurred from uncompleted mission work and losses of components. A new numerical algorithm is proposed to assess the EML of the considered multi-attempt loading-dependent mission system under any SLAP. Based on the EML evaluation, the genetic algorithm is implemented to solve the proposed optimization problem. A case study of a fleet of aerial vehicles performing a delivery mission is provided to showcase the proposed model and explore the impacts of several key model parameters on the EML and optimal SLAP solutions.