| Full text | |
| Author(s): |
Neubauer, Thais Rodrigues
;
da Silva, Valdinei Freire
;
Fantinato, Marcelo
;
Peres, Sarajane Marques
;
Xavier-Junior, JC
;
Rios, RA
Total Authors: 6
|
| Document type: | Journal article |
| Source: | INTELLIGENT SYSTEMS, PT I; v. 13653, p. 16-pg., 2022-01-01. |
| Abstract | |
Resource allocation to execute business processes is increasingly crucial for organizations. As the cost of executing process tasks relies on several dynamic factors, optimizing resource allocation can be addressed as a sequential decision process. Process mining can aid this optimization with the use of data from the event log, which records historical data related to the corresponding business process executions. Probabilistic approaches are relevant to solve process mining issues, especially when applied to the usually unstructured and noisy real-world business processes. We present an approach in which the problem of resource allocation in a business process is modeled as a Markovian decision process and batch reinforcement learning algorithm is applied to get a resource allocation policy that minimizes the cycle time. With batch reinforcement learning algorithms, the knowledge underlying the event log data is used both during policy learning procedures and to model the environment. Resource allocation is performed considering the task to be executed and the resources' current workload. The results with both Fitted Q-Iteration and Neural Fitted Q-Iteration batch reinforcement learning algorithms demonstrate that this approach enables a resource allocation more adherent to the business interests. Per the evaluation we performed on data of a real-world business process, if our approach had been used, up to 37.2% of the time spent to execute all the tasks could have been avoided compared to what is represented in the historical data at the event log. (AU) | |
| FAPESP's process: | 20/05248-4 - Enhancing the X-Processes algorithm to discover more accurate process models |
| Grantee: | Marcelo Fantinato |
| Support Opportunities: | Regular Research Grants |