Abstract
System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection.
Highlights
Joint operations have become the main trend of modern warfare
The non-dominated sorting generic algorithm (NSGA) is a kind of widely used multi-objective algorithm, which exhibits a good performance for retaining elites in offspring
The weight information of capabilities is determined by analyzing correlations between capabilities and the intercepted missile numbers, based on operation simulation data
Summary
Joint operations have become the main trend of modern warfare. The construction of “system of systems (SoS)” is a goal and a basic guideline on the long-term weapon/equipment development. System portfolio selection is a widely used concept of weapon SoS construction, where a key step is evaluation [1]. Traditional evaluation models rely too much on subjective awareness, making assessment results inaccurate and unconvincing to some extent. With the rise of data science, an effective method to compensate for the low accuracy and implementing difficulty of relying on expert experience is making decisions according to real data. The combination of data-driven methods and model-based approaches is a new trend to solving system portfolio selection problems
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