Some business process management systems (BPMSs) have been developed in the field of smart factories. These systems are typically based on technical or production areas and technical processes. However, many existing systems, with respect to technologies used in smart factories and also the dynamic nature of the processes in these environments, are not able meet requirements of smart factories in the business process execution. The present study presents a new prototype of BPMS architecture based on smart factories’ characteristics. This prototype has several components. In the monitoring component, process management can take place through process mining techniques inside a defined data analysis system for collecting event logs from big data. This component could operate based on control and optimization modules. The control module is applied to discover process models and their conformity with models extracted from business process analysis using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Adaptive Boosting (AdaBoost) algorithms. Also, the optimization module can improve the processes model based on Business Process Intelligence (BPI) technique and Key Performance Indicators (KPIs). The results of the new prototype execution on a case study indicate that the proposed architecture is highly accurate, complete, and optimal in process management for smart factories.

Some business process management systems (BPMSs) have been developed in the field of smart factories. These systems are typically based on technical or production areas and technical processes. However, many existing systems, with respect to technologies used in smart factories and also the dynamic nature of the processes in these environments, are not able meet requirements of smart factories in the business process execution. The present study presents a new prototype of BPMS architecture based on smart factories’ characteristics. This prototype has several components. In the monitoring component, process management can take place through process mining techniques inside a defined data analysis system for collecting event logs from big data. This component could operate based on control and optimization modules. The control module is applied to discover process models and their conformity with models extracted from business process analysis using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Adaptive Boosting (AdaBoost) algorithms. Also, the optimization module can improve the processes model based on Business Process Intelligence (BPI) technique and Key Performance Indicators (KPIs). The results of the new prototype execution on a case study indicate that the proposed architecture is highly accurate, complete, and optimal in process management for smart factories.

Business Process Management Systems (BPMS) is a complex information system that provides designing, administrating, and improving the business processes. Task allocation to human resources is one of the most important issues which should be managed more efficiently in BPMS. Task allocation algorithms are defined in order to meet the various policies of organizations. The most important of these policies could be reducing the average cycle time, balancing the resource workload, increasing the product quality and minimizing the production costs. Therefore, choosing an appropriate resource in task allocation algorithms could influence on overall policy of the organization. In heavy load conditions or when the number of human resources is limited, workload balancing can increase the stability of the system. In this paper, a task allocation algorithm is proposed to rebalance the resource workload in runtime while minimizing cycle time by offering dependent pair tasks to resources for concurrent processing. The experimental results show that the combination of previous algorithms with the proposed algorithms would have 4. 42% reduction in cycle time in contrast to most efficient stateof-the-art algorithms.