Flow graph with non zero lower bound or 0 capacity I am afraid the question title might not be sufficiently accurate but I could not come up with something more accurate
Here is the problem
Given 'n' machines

Each machine has a set of capabilities
Each machine has max availability (A(m))

Given 'm' tasks

Each task requires a set of capabilities
Each task takes a certain time (D(t))
A task has to be performed on one machine only

The problem is to determine whether all tasks can be completed.
I get stuck with the 'one machine only' requirement. The only flow graphs I can come up with do not guarantee a task is not linked to more than one machine.
It's sort of a bipartite matching problem but with capacities &gt; 1
I also ran into XOR-like behavior in flow networks which is similar but has the 'xor' requirement on the 'source' end where I would need it on the target end.
Would anyone have any tips? Is it at all possible to model this as a flow graph?
Tx!
Peter
PS Trying to clarify requirements with more concrete example
Assume digital print systems and print jobs

Each digital press can run for a number of hours
Each press has some finishing possibilities: e.g. 'sheet cutter', 'laminate', 'laser cutter', 'page folding', ....
Each print job requires a number of hours
Each print job needs one or more of the finishing possibilities

Given a set of machines, the availability for each and the finishing possibilities and also a set of print jobs (duration, finishing options needed) determine whether all print jobs can finish
So e.g.

Printer p1 is available for a duration of 10 hours and has features f1 and f2
Printer p2 is available for a duration of 10 hours and has features f2 and f3
Job1, requiring features f1 and f2 takes 8 hours to run
Job2, requiring features f2 and f3 takes 8 hours to run
Job3, requiring feature f2 requires 4 hours to run

A printer that is available for e.g. 10 hours can run 10 x 1 hour jobs or 5 x 2 hour jobs, or 1 x 8 hr job and 1 x 2 hr job, ... Jobs always have to run on a single printer
The flow diagrams I could come up with always result in cases where
8 hours of p1 is assigned to job1 (leaving 2 hours for printer p1)
8 hours of p2 is assigned to job2 (leaving 2 hours for printer p2)
(so far so good)
but then
The 2 hours of p1 and p2 left are used to flow to job3 and the max flow seems to indicate the three jobs can be run (which is not ok)
Your problem is NP-hard.  In the special case where no job requires any particular features, and all printers have the same availability, this becomes just the bin packing problem, which is (strongly) NP-complete.
You could try adapting standard algorithms for bin packing to your situation.  For instance, one approach would be to use integer linear programming and hope the ILP solver can handle the resulting problem instance.