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

Hao Song; Xiaonong Lu; Xu Zhang; Xiaoan Tang; Qiang Zhang; Hao Song; Xiaonong Lu; Xu Zhang; Xiaoan Tang; Qiang Zhang

doi:10.3934/mbe.2020355

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 6838-6872. doi: 10.3934/mbe.2020355

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Research article Special Issues

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

1.
School of Management, Hefei University of Technology, Hefei 230009, China
2.
Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China

Received: 30 June 2020 Accepted: 23 September 2020 Published: 09 October 2020

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 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.
- cloud manufacturing,
- service composition and optimal selection,
- HRGO,
- quality of service,
- fuzzy similarity degree
Citation: Hao Song, Xiaonong Lu, Xu Zhang, Xiaoan Tang, Qiang Zhang. A HRGO approach for resilience enhancement service composition and optimal selection in cloud manufacturing[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6838-6872. doi: 10.3934/mbe.2020355

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Abstract

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 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.

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