Risk integration and optimization of oil-importing maritime system: a multi-objective programming approach
Optimization of oil-imports portfolio has attracted considerable attention from corporate operators as well as government and its strategic planners. This paper proposes a methodology of oil-importing portfolio optimization based on the fundamental process of intelligent knowledge management (IKM). Centering on the maritime system, optimal solutions are derived for different risk scenarios. Specifically, three main steps are involved: formulating a multi-objective programming (MOP) model, integrating the composite risk exposure with domain knowledge, as well as knowledge acquisition on risk scenarios and influence of transportation risk. For illustration, optimization of the maritime structure of Chinas oil imports is performed to verify the practicability of the novel methodology. Experimental results suggest that the risk-adjusted factors augmentation can spread the risk wider and eventually enhance risk optimization capability in the MOP model. With a given risk-adjusted factor, the influence of transportation risk on an optimal plan is simulated and analyzed. The paper uses the fundamental IKM process for transforming the data (rough knowledge) into intelligent knowledge (transformation from T1 to T2) in the empirical study on risk integration and optimization of oil-importing maritime system. It is helpful to explore hidden patterns. Whats more, results suggest that it is necessary to highlight the influence of transportation risk in order to support decision makers from different domains to obtain more reasonable optimal solutions.
risk integration multi-objective programming intelligent knowledge management transportation risk country risk