  Engineering regulatory compliance in complex Cyber-Physical Systems (CPS),
such as smart warehouse logistics, is challenging due to the open and dynamic
nature of these systems, scales, and unpredictable modes of human-robot
interactions that can be best learnt at runtime. Traditional offline approaches
for engineering compliance often involve modelling at a higher, more abstract
level (e.g. using languages like SysML). These abstract models only support
analysis in offline-designed and simplified scenarios. However, open and
complex systems may be unpredictable, and their behaviours are difficult to be
fully captured by abstract models. These systems may also involve other
business goals, possibly conflicting with regulatory compliance. To overcome
these challenges, fine-grained simulation models are promising to complement
abstract models and support accurate runtime predictions and performance
evaluation with trade-off analysis. The novel contribution of this work is a
Digital Twin-oriented architecture for adaptive compliance leveraging abstract
goal modelling, fine-grained agent-based modelling and runtime simulation for
managing compliance trade-offs. A case study from smart warehouse logistics is
used to demonstrate the approach considering safety and productivity
trade-offs.
