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为研究不确定型区间风险数据情境下的危险品运输网络风险规避问题,采用最小最大准则,构建一个具有更强鲁棒性的危险品运输网络双层规划模型。结合求解最短路径的Dijkstra算法,设计出启发式算法,处理不确定型区间风险数据,通过实例求解并验证模型的有效性。实例测试结果表明:算法程序在运行过程中始终能够达到一种稳定状态,能够删除那些具有最大风险上界值的边,促使企业在运输危险品时,选择远离人口密集区域的运输路线,求解得到的危险品运输鲁棒性网络,可以解决危险品运输过程中路段产生风险波动的问题。
In order to study the risk aversion of dangerous goods transport network in the context of uncertain interval risk data, a two-level programming model of the transport network of dangerous goods with more robustness is constructed using the minimum and maximum criteria. Combined with the Dijkstra algorithm to solve the shortest path, a heuristic algorithm is designed to deal with the uncertain interval risk data, and the validity of the model is verified through examples. The results of the example tests show that the algorithm program can always reach a steady state during operation, and can remove the edges with the upper bound of the maximum risk, and encourage the enterprises to select the transportation route away from densely populated areas when transporting dangerous goods. The robust network of dangerous goods transport can solve the problem of risk fluctuation caused by road sections during the transport of dangerous goods.