Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type o...Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type or pairwise interactions and incorporated only a limited range of influencing factors,thus facing substantial challenges in accurately capturing high-dimensional,heterogeneous interactions while maintaining computational tractability in large-scale problems.To overcome these limitations,this paper proposes a more reliable risk response decision-making framework capable of delivering precise solutions for large-scale decision problems.Specifically,the main contributions of this study encompass the following three aspects:(1)A comprehensive risk response decision model is developed to concurrently integrate multiple types of risk interactions and critical factors,thereby better reflecting real-world conditions and enhancing decision quality.(2)To mitigate the computational burden arising from the exponential growth of nonlinear terms,an equivalent formulation based on risk scenario decomposition is introduced,enabling the precise resolution of large-scale instances.(3)A Dynamic Infeasibility-Guided Genetic Algorithm is designed to efficiently generate high-quality solutions for extra-large-scale problems.Experimental evaluations demonstrate that the proposed algorithm consistently outperforms traditional approaches in terms of solution quality,stability,and computational efficiency across thousand-dimensional problem instances,underscoring its strong performance and scalability.This research provides a scalable and effective solution framework for complex risk response decision-making,offering valuable insights for large-scale risk management applications.展开更多
The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary me...The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake） at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives： maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.展开更多
基金funded by the National Natural Science Foundation of China(Grant numbers:72271144,72134004)China Postdoctoral Science Foundation(Grant number:2022T150379)Project of Young Talents Team for Philosophy and Social Sciences in Shandong Province(Grant number:2024-QNRC-01).
文摘Effective risk response decision-making in complex contexts often necessitates addressing multiple interacting risk events under resource constraints.However,existing models have predominantly focused on single-type or pairwise interactions and incorporated only a limited range of influencing factors,thus facing substantial challenges in accurately capturing high-dimensional,heterogeneous interactions while maintaining computational tractability in large-scale problems.To overcome these limitations,this paper proposes a more reliable risk response decision-making framework capable of delivering precise solutions for large-scale decision problems.Specifically,the main contributions of this study encompass the following three aspects:(1)A comprehensive risk response decision model is developed to concurrently integrate multiple types of risk interactions and critical factors,thereby better reflecting real-world conditions and enhancing decision quality.(2)To mitigate the computational burden arising from the exponential growth of nonlinear terms,an equivalent formulation based on risk scenario decomposition is introduced,enabling the precise resolution of large-scale instances.(3)A Dynamic Infeasibility-Guided Genetic Algorithm is designed to efficiently generate high-quality solutions for extra-large-scale problems.Experimental evaluations demonstrate that the proposed algorithm consistently outperforms traditional approaches in terms of solution quality,stability,and computational efficiency across thousand-dimensional problem instances,underscoring its strong performance and scalability.This research provides a scalable and effective solution framework for complex risk response decision-making,offering valuable insights for large-scale risk management applications.
基金supported by the National Natural Science Foundation of China 71371181 91024006China Postdoctoral Science Foundation (2012M521918)
文摘The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters （which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake） at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives： maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.
