摘要
针对具有不确定性、复杂性的大规模运输网络,以各种概率分布函数的运输路径优化问题为研究对象,通过具体的网络实例证明传统最优路径方法的局限性,提出了一种用于搜索随机运输网络中最优路径的频域生成图模型(Frequency-domain Spanning Graph,FSG),并给出其求解大规模运输网络路径优化的算法.FSG通过时频域间概率函数的相互转化,能够定量处理连续概率分布和离散经验分布两种形式,其大规模分层搜索算法易于计算机化,实现效率高.最后给出的大规模运输网络路径优化算例,验证了模型和算法的可行性和有效性,显示出良好的应用前景.
To optimize transportation path with various probability distribution function in large-scale transportation network, the uncertainty and complexity characteristic of large-scale transportation network are studied. Due to the limitations of classical algorithms for shortest path analysis, a novel frequencydomain spanning graph model for searching optimal path in stochastic and large-scale transportation network is presented, and its corresponding algorithm is designed to deal with the problem. Through the mutual transformation of probability function between time-domain and frequency-domain in our model, the optimal path of origin-destination is analyzed quantitatively. An advantage of our approach is that it can handle uniformly both the continuous probability distribution case and the discrete probability distribution case, and its algorithm for the large-scale transportation network is high effective and easy to simulate by MATLAB. Finally, numerical experiment results illustrate the feasibility and effectiveness of our model and algorithm and show a good application prospect.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2009年第10期85-93,共9页
Systems Engineering-Theory & Practice
关键词
大规模网络
随机运输
最优路径
频域生成图
large-scale network
stochastic transportation
optimal path
frequency-domain spanning graph
