Pareto-Optimal Trace Generation from Declarative Process Models

Declarative process models (DPMs) enable the description of business process models with a high level of flexibility by being able to describe the constraints that compliant traces must abide by. In this way, a well-formed declarative specification generates a family of compliant traces. However, little is known about the difference between different compliant traces, as the only criterion used for comparison is satisfiability. In particular, we believe that not all compliant traces are alike: some might be sub-optimal in their resource usage. In this work, we would like to support users of DPMs in the selection of compliant and optimal traces. In particular, we use Dynamic Condition Response (DCR) graphs as our language to represent DPMs, extending it with a parametric definition of costs linked to events. Multiple types of cost imply that different traces might be optimal, each according to a different cost dimension. We encode cost-effective finite trace generation as a Constraint Optimisation Problem (COP) and showcase the feasibility of the implementation via an implementation in MiniZinc. Our initial benchmarks suggest that the implementation is capable of providing answers efficiently for processes of varying size, number of constraints, and trace length.