Mission success probability optimizing of phased mission system balancing the phase backup and system risk: A novel GERT mechanism

Mission success probability (MSP) is an important indicator of performance of phased mission system (PMS), representing the ability to complete the mission successfully. Failure accumulates during the mission, and may lead to a complete system breakdown. A phase backup strategy can somewhat improve mission success, but the potential risks and coupling effects in backup processes may jeopardize system survivability and mission success. To address this issue, a novel Graphical Evaluation Review Technique (GERT) mechanism is devised in this paper to evaluate and optimize the MSP of PMS by balancing the phase backup and system risk. Precisely, the GERT network model is established according to the characteristics of the mission process and backup plan, and the transfer properties of the network structures are analyzed. Based on this, a new risk transmittance parameter is designed to infer system risk, evaluate mission-related parameters, and rank the risk of different phases. The phase backup strategy is filtered for optimal MSP utilizing tag method and conditional moment generating function, with constraints on some risk-pertinent parameters. Finally, a case study on a UAV swarm's coastal offensive and defensive operation mission is performed to validate the theoretical findings.

Automated summary

This paper devises a novel Graphical Evaluation Review Technique (GERT) mechanism to evaluate and optimize the Mission Success Probability (MSP) of Phased Mission System (PMS) by balancing phase backup and system risk. A GERT network model is established and the transfer properties of the network structures are analyzed. A new risk transmittance parameter is designed to infer system risk, evaluate mission-related parameters, and rank the risk of different phases. The phase backup strategy is filtered for optimal MSP using tag method and conditional moment generating function, with constraints on some risk-pertinent parameters. A case study on a UAV swarm's coastal offensive and defensive operation mission is performed to validate the theoretical findings.