In collaboration scenarios, we often encounter situations in
which semantically interrelated models are changed concurrently.
Concurrent model synchronization denotes the task
of keeping these models consistent by propagating changes
between them. This is challenging as changes can contradict
each other and thus be in conflict. A problem with current
synchronisation approaches is that they are often nondeterministic,
i.e., the order in which changes are propagated
is essential for the result. Furthermore, a common limitation
is that the involved models must have been in a consistent
state at some point, and that the applied changes are at least
valid for the domain in which they were made. We propose
a hybrid approach based on Triple Graph Grammars (TGGs)
and Integer Linear Programming (ILP) to overcome these issues:
TGGs are a grammar-based means that supplies us with
a superset of possible synchronization solutions, forming a
search space from which an optimum solution incorporating
user-defined preferences can be chosen by ILP. Therefore, the
proposed method combines configurability by comprising
expert knowledge via TGGs with the flexible input handling
of search-based techniques: By accepting arbitrary graph
structures as input models, the approach is tolerant towards
errors induced during the modelling process, i.e., it can cope
with input models which do not conform to their metamodel
or which cannot be generated by the TGG at hand. The
approach is implemented in the model transformation tool
eMoflon and evaluated regarding scalability for growing
model sizes and an increasing number of changes.