Self-decision mechanisms of smart production systems based on improved uncertainty theory and user-CFA

Abstract

Smart production systems (SPSs) are complex smart network systems composed of multiple smart objects that require a self-decision-making mechanism for quick and accurate execution and decision making of dynamic smart production tasks. However, there is currently no uniform self-decision mechanism to handle the dynamic uncertainty of multi-smart object behaviors and ensure SPSs stability. To address this problem, this paper proposes a comprehensive self-decision method for SPSs that considers users’ preferences. A common technological framework for SPSs, a self-decision feature tree, and a self-decision mechanism with three modules are proposed in this paper to define conditions of dynamic smart production scheme (DSPs), optimize DSPs, and recommend DSPs. Autonomic computing methods and improved uncertainty theory are used to achieve real-time optimization and satisfy multi-objective performance requirements. Additionally, a collaborative filtering algorithm integrated with basic user preferences (user-CFA) is proposed to enable autonomous scheme recommendations. Finally, the effectiveness and autonomous operation of self-decision method is demonstrated through a case study on multi-Automated Guided Vehicles (AGVs), where we compare it with conventional methods. The results confirm the advantages of our proposed self-decision method, which contributes to the development and improvement of SPSs.