networkx.algorithms.flow.min_cost_flow_cost¶

min_cost_flow_cost
(G, demand='demand', capacity='capacity', weight='weight')[source]¶ Find the cost of a minimum cost flow satisfying all demands in digraph G.
G is a digraph with edge costs and capacities and in which nodes have demand, i.e., they want to send or receive some amount of flow. A negative demand means that the node wants to send flow, a positive demand means that the node want to receive flow. A flow on the digraph G satisfies all demand if the net flow into each node is equal to the demand of that node.
 Parameters
G (NetworkX graph) – DiGraph on which a minimum cost flow satisfying all demands is to be found.
demand (string) – Nodes of the graph G are expected to have an attribute demand that indicates how much flow a node wants to send (negative demand) or receive (positive demand). Note that the sum of the demands should be 0 otherwise the problem in not feasible. If this attribute is not present, a node is considered to have 0 demand. Default value: ‘demand’.
capacity (string) – Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: ‘capacity’.
weight (string) – Edges of the graph G are expected to have an attribute weight that indicates the cost incurred by sending one unit of flow on that edge. If not present, the weight is considered to be 0. Default value: ‘weight’.
 Returns
flowCost – Cost of a minimum cost flow satisfying all demands.
 Return type
integer, float
 Raises
NetworkXError – This exception is raised if the input graph is not directed or not connected.
This exception is raised in the following situations:
The sum of the demands is not zero. Then, there is no flow satisfying all demands.
There is no flow satisfying all demand.
NetworkXUnbounded – This exception is raised if the digraph G has a cycle of negative cost and infinite capacity. Then, the cost of a flow satisfying all demands is unbounded below.
Notes
This algorithm is not guaranteed to work if edge weights or demands are floating point numbers (overflows and roundoff errors can cause problems). As a workaround you can use integer numbers by multiplying the relevant edge attributes by a convenient constant factor (eg 100).
Examples
A simple example of a min cost flow problem.
>>> G = nx.DiGraph() >>> G.add_node("a", demand=5) >>> G.add_node("d", demand=5) >>> G.add_edge("a", "b", weight=3, capacity=4) >>> G.add_edge("a", "c", weight=6, capacity=10) >>> G.add_edge("b", "d", weight=1, capacity=9) >>> G.add_edge("c", "d", weight=2, capacity=5) >>> flowCost = nx.min_cost_flow_cost(G) >>> flowCost 24