Service composition model and method in cloud manufacturing

Abstract This study optimizes service composition on the basis of task requirements to solve the problem of multitask corresponding multi-service selection. First, the basic path structure and the implementation steps of cloud manufacturing (CMfg) service composition are analyzed, and service composition is divided into four patterns. Second, the quality of service (QoS) index system of service composition is proposed by combining the six goals of time, composability, quality, usability, reliability, and cost; the calculation expressions of QoS under different composition structures are listed; and the mathematical model of CMfg service composition is established. Then, the weight of each index value in QoS evaluation is determined using an improved fuzzy comprehensive evaluation method. Finally, the optimal selection scheme of service composition is proposed by using gray relational analysis method(GM), and the validity of the optimal selection scheme is verified by an example of mold manufacturing.

[1]  Dechen Zhan,et al.  Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm , 2015 .

[2]  Feng Li,et al.  A clustering network-based approach to service composition in cloud manufacturing , 2017, Int. J. Comput. Integr. Manuf..

[3]  Feng Xiang,et al.  The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system , 2016 .

[4]  Hamed Bouzary,et al.  Service optimal selection and composition in cloud manufacturing: a comprehensive survey , 2018 .

[5]  Yu Xue,et al.  Self-adaptive bat algorithm for large scale cloud manufacturing service composition , 2018, Peer-to-Peer Netw. Appl..

[6]  Xifan Yao,et al.  Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition , 2017, Applied Intelligence.

[7]  Shuai Zhang,et al.  Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation , 2018, Int. J. Comput. Integr. Manuf..

[8]  Xifan Yao,et al.  Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing , 2017, Appl. Soft Comput..

[9]  Li-Nan Zhu,et al.  IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing , 2019, Complex..

[10]  Xifan Yao,et al.  Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm , 2016 .

[11]  Liu Jian,et al.  An approach for service composition optimisation considering service correlation via a parallel max–min ant system based on the case library , 2018, Int. J. Comput. Integr. Manuf..

[12]  Xifan Yao,et al.  An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing , 2018, Inf. Sci..

[13]  Haibo Li,et al.  Composition of Resource-Service Chain for Cloud Manufacturing , 2016, IEEE Transactions on Industrial Informatics.

[14]  Yaghoub Farjami,et al.  An ensemble optimisation approach to service composition in cloud manufacturing , 2019, Int. J. Comput. Integr. Manuf..

[15]  Ray Y. Zhong,et al.  Workload-based multi-task scheduling in cloud manufacturing , 2017 .

[16]  Lei Ren,et al.  Manufacturing service composition model based on synergy effect: A social network analysis approach , 2018, Appl. Soft Comput..

[17]  Xun Xu,et al.  From cloud computing to cloud manufacturing , 2012 .

[18]  Zhanwei Hou,et al.  An Approach for Multipath Cloud Manufacturing Services Dynamic Composition , 2017, Int. J. Intell. Syst..

[19]  Bin Xu,et al.  A fuzzy operator based bat algorithm for cloud service composition , 2016, Int. J. Wirel. Mob. Comput..

[20]  Ting He,et al.  An Approach to Iot Service Optimal Composition for Mass Customization on Cloud Manufacturing , 2018, IEEE Access.

[21]  Xun Xu,et al.  A semantic web-based framework for service composition in a cloud manufacturing environment , 2017 .

[22]  Xifan Yao,et al.  Correlation-aware QoS modeling and manufacturing cloud service composition , 2017, J. Intell. Manuf..

[23]  Xun Xu,et al.  Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services , 2019, Robotics and Computer-Integrated Manufacturing.

[24]  Yefa Hu,et al.  QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system , 2014, Central Eur. J. Oper. Res..

[25]  W. Art Chaovalitwongse,et al.  Manufacturing Resource Modeling for Cloud Manufacturing , 2017, Int. J. Intell. Syst..

[26]  José L. Martínez Lastra,et al.  Cloud computing as a facilitator for web service composition in factory automation , 2019, J. Intell. Manuf..

[27]  Fateh Seghir,et al.  A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition , 2018, J. Intell. Manuf..

[28]  Xiao Xue,et al.  Manufacturing service composition method based on networked collaboration mode , 2016, J. Netw. Comput. Appl..

[29]  Mohammad Sadegh Aslanpour,et al.  CSA-WSC: cuckoo search algorithm for web service composition in cloud environments , 2018, Soft Comput..

[30]  Paulo E. Miyagi,et al.  Service Composition in the Cloud-Based Manufacturing Focused on the Industry 4.0 , 2015, DoCEIS.