Process Mining and Simulation for a p-Time Petri Net Model with Hybrid Resources

Abstract

Process mining can be understood as a tool to extract useful information from processes that already happened and make decisions to improve performance of processes. The main three techniques while applying process mining to event logs are process discovery, process enhancement and conformance checking. Among many different applications that process mining can be applied to, in this paper, process mining is used to discover the model from event logs generated from simulations of the "Handle Complaint Process" Workflow net based on a p-time Petri net model with hybrid resources. This net discovered with process mining must be similar to the original one due event logs used to generate it are created from the simulation. There is no doubt that process mining has become increasingly useful for the future of Workflow nets especially when it is followed by simulation. Process mining can discover processes from event logs, find deviations and produce a better workflow while simulation can test new scenarios and hypotheses. With both working together product owners can reach a pretty good process excellence. The "Handle Complaint Process" Workflow net based on a p-time Petri net model with hybrid resources tries to solve the real time scheduling problem of Workflow Management Systems. The approach made in this work in particular, utilizes discrete + continuous resources and real time to decide when to fire a transition in the Workflow net. To generate the event logs from the simulation of the Workflow net, some functions were added in order to capture the identification number of each token, the path made by it, as well as the timestamp in the moment the transition was fired and the person or system responsible for the activity. This Workflow net was simulated using CPN Tools. The logs generated from the simulation were converted using the ProM Import tool and the process mining discovery technique was applied using ProM. The use of event logs of a business process model is a way to detect deviations from the expected behavior. Based on these deviations, the process can be changed in order to achieve excellence. The logs from the p-net model with hybrid resources tries to simulate, in a better way, the human behavior. As the model generated from the logs is similar to the original one, the conversion is correct. As a future work proposal, we will compare a real event log with the results achieved with this work to see the efficiency in simulating a process model with hybrid resources.