Optimal transport on supply-demand networks
arXiv:0908.1184 · doi:10.1103/PhysRevE.81.066105
Abstract
Previously, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grid and supply chain networks, show a far different scenario in which the nodes are classified into two categories: the supply nodes provide some kinds of services, while the demand nodes require them. In this paper, we propose a general transport model for those supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogenous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the optimal configuration of supply nodes, which remarkably enhances the transport capacity, and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.
5 pages, 1 table and 4 figures
References in corpus (16)
- Finding community structure in networks using the eigenvectors of matrices
- Robust dynamic classes revealed by measuring the response function of a social system
- Effective and Efficient Similarity Index for Link Prediction of Complex Networks
- Efficient routing on complex networks
- The Rich-Club Phenomenon In The Internet Topology
- Navigability of Complex Networks
- Optimal routing on complex networks
- Role of Activity in Human Dynamics
- Communication Bottlenecks in Scale-Free Networks
- Transport on Complex Networks: Flow, Jamming and Optimization
- Immunization of Susceptible-Infected Model on Scale-Free networks
- The effect of bandwidth in scale-free network traffic
- Search in Complex Networks : a New Method of Naming
- Comment on ``Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality"
- Transport in networks with multiple sources and sinks
- Mixing navigation on networks