# Network flow as a linear/integer programming problem with special conditional constraints

Consider the classic network flow problem where the constraint is that the inflow to a vertex is equal to the sum of its outflows. Consider having a more specific constraint where the flow cannot be split between edges. The constraint is that the inflow a to a vertex v is equal to b (the amount that v has acquired from a) plus c (the outflow carried by only one of the non-visited edges of v).

The figure below demonstrates this in 3 mutually exclusive cases. The green directed edge carries an inflow of value a. Vertex b takes some value of a, then only one of the edges (disregarding the inflow edge) carries the outflow of value c=a-b (illustrated in 3 cases - the red edge represents the edge carrying the outflow, the rest of the edges should have zero outflow).

Is this constraint possible to formulate under the linear/integer programming setting ?

I would think that you need a MIP formulation for that. What about the following? Assume directed arcs (this means all our flows are non-negative: $x_{i,j}\ge 0$). First write down the in-flow=out-flow balance equation:
$$\sum_i x_{i,k} = u_k + \sum_j x_{k,j} \> \forall k$$
Here $u_k$ is what is consumed by node $k$ (could be a variable or a coefficient; not sure from the description). Then add binary variables and constraints:
$$\begin{array}{ll} x_{k,j} \le M_{k,j} \delta_{k,j} & \forall k,j\\ \sum_j \delta_{k,j} = 1 & \forall k \\ \delta_{k,j} \in \{0,1\} \\ \end{array}$$ The coefficient $M_{k,j}$ should be chosen as small as possible (i.e. equal to the upperbound or capacity of arc $x_{k,j}$). In this case, instead of binary variables you could use SOS1 sets to model this (I usually choose the formulation with binary variables).