P-MDP : A Framework to Optimize NFPs of Business Processes in Uncertain Environments

Abstract

In uncertain environments, compliance assurance for business processes faces the intertwined challenges posed by diverse non-functional properties (NFPs) and complex gateways in the models. While solutions like user-defined metrics and constraints empower businesses to act independently, or Markov Decision Process (MDP) enhance statistical algorithm design, they address uncertainties from different views and have not been considered together to tackle a holistic, integrated business and system uncertainties. This paper proposes Process-aware MDP (P-MDP), a framework that unifies the two divergent view to optimize NFPs for business processes operating under uncertainty. We devise a gateway-aware, lazy-evaluation reward mechanism supporting the key stakeholders – business managers – to customize metrics, algorithms, and constraints, and apply them on business processes. Experiments with the WSDREAM benchmark dataset show that P-MDP outperforms the state-of-the-art (SOTA) method constraint-satisfied service composition MDP (CSSC-MDP) at various scales. Moreover, P-MDP demonstrates superior generality and scalability, enabling stakeholders to generate better execution plans for business processes in complex scenarios.