Optimal autonomous architecture for uncertain processes management

An uncertain Business Process Management System (BPMS) capability is Business Processes (BPs) management in the presence of uncertain factors. This ability should be defined by different uncertain computer-based components inside the classic BPMSs operations. This study proposed autonomous and combinatorial optimal process management architecture to increase the ability, flexibility, and accuracy of uncertain processes management. The autonomous architecture based on the bi-level optimization approach has been constructed inward a meta-model of multi-agent system technology, optimal Neural Network and Cellular Learning Automata in different agents. A case study of an uncertain business process evolving the closed loop supply chain was studied. The results of the simulated case and the statistical evaluation of it, have been demonstrated the robustness and accuracy of this new proposed architecture. (C) 2019 Elsevier Inc. All rights reserved.