A HRGO approach for resilience enhancement service composition and optimal selection in cloud manufacturing

Cloud manufacturing (CM) establishes a collaborative manufacturing services chain among dispersed producers, which enables the efficient satisfaction of personalized manufacturing requirements. To further strengthen this effect, the manufacturing service composition and optimal selection (SCOS) in CM, as a NP-hard combinatorial problem, is a crucial issue. Quality of service (QoS) attributes of manufacturing services, as the basic criterion of functions and capabilities, are decisive criterions of SCOS. However, most traditional QoS attributes of CM ignore the dynamic equilibrium of manufacturing services and only rely on initial static characterizations such as reliability and availability. In a high uncertainty and dynamicity environment, a major concern is the equilibrium of manufacturing services for recovering their functions after dysfunctional damage. Therefore, this paper proposes a hybrid resilience-aware global optimization (HRGO) approach to address the SCOS problem in CM. This approach helps manufacturing demanders to acquire efficient, resilient, and satisfying manufacturing services. First, the problem description and resilience measurement method on resilience-aware SCOS is modeled. Then, a services filter strategy, based on the fuzzy similarity degree, is introduced to filter redundant and unqualified candidate services. Finally, a modified non-dominated sorting genetic algorithm (MNSGA-III) is proposed, based on diversity judgment and dualtrack parallelism, to address combination optimization step processing in SCOS. A series of experiments were conducted, the results show the proposed method is more preferable in optimal services searching and more efficient in scalability.