Fuzzy random shortest path problem using conditional Value at Risk
Fuzzy random shortest path problem using conditional Value at Risk
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DOI：
10.1109/icsse.2010.5551772
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
T. Hasuike
中科院分区：
文献类型：
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作者：
T. Hasuike
本文考虑模糊随机最短路问题，提出一种新的风险度量，综合了模糊随机条件风险值和可信性度量。将混合条件风险价值定义的模型等价转化为0-1混合整数规划问题。为了高效解析地求解该问题，利用全么模的性质，对拉格朗日0-1松弛问题提出了基于标准Dijkstra算法和次梯度法混合算法的高效求解算法。
This paper considers a fuzzy random shortest path problem and proposes a new risk measure to synthesize both stochastic conditional Value at Risk and credibility measure for fuzziness. The proposed model defined by the hybrid conditional Value at Risk is equivalently transformed into a 0–1 mixed integer programming problem. In order to this problem analytically and efficiently, the Lagrange 0–1 relaxation problem using the property of totally unimodular and proposed the efficient solution algorithm based on the hybrid algorithm of standard Dijkstra algorithm and subgradient method.