Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing
Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users’ multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2018-06, Vol.96 (9-12), p.4455-4465 |
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| container_issue | 9-12 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Que, Yi Zhong, Wei Chen, Hailin Chen, Xinan Ji, Xu |
| description | Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users’ multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. The results prove the effectiveness and superiority of the proposed algorithm and its suitability for solving large-scale service composition problems. |
| doi_str_mv | 10.1007/s00170-018-1925-x |
| format | Article |
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As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. 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| source | Springer Nature - Complete Springer Journals |
| subjects | Adaptive algorithms CAE) and Design Composition Computer-Aided Engineering (CAD Construction costs Engineering Entropy (Information theory) Genetic algorithms Industrial and Production Engineering Information technology Manufacturing Mechanical Engineering Media Management Original Article Performance indices Reliability Resource management Service areas User satisfaction |
| title | Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing |
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