Abstract
Autonomous and cooperative elements of Cyber-physical production systems (CPPSs) which rely on the inevitable interconnection with digital and physical components to make intelligent and profitable production processes have made these systems more complex and chaotic in prediction. The increasing complexity, heterogeneity, and dynamism of CPPSs underscore the importance of resilience capabilities within such systems. The interconnection architecture and networking of physical and digital parts play a key role in the design and development of resilient CPPSs. To address these interactions, decrease the complexity, and increase the robustness simultaneously, identifying the right Design Parameters (DPs) and alternatives is significantly important. DPs are solutions that fulfill higher-level Functional Requirements (FRs) in the physical domain of the CPPSs’ design. Despite numerous literature on CPPSs and advancements in this field, less attention has been paid to exploring and classifying resilience FRs and related DPs. In this context, this paper presents a comprehensive review to determine the DPs considered in the scientific literature to enhance the resilience of CPPSs. The identified DPs are classified as physical solutions, digital solutions, and an integration of both perspectives. The results show various types of sensors, machine learning, artificial intelligence, and digital twins are the most implemented DPs to fulfill FRs on robustness, adaptability, self-regulation, self-recovery, autonomous decision-making, and interoperability, thereby providing resilience capabilities in CPPSs.