Service matching and selection in cloud manufacturing: a state-of-the-art review

Abstract Cloud manufacturing (CMfg) is a new service-oriented networked manufacturing paradigm, in which sharing and collaborating among distributed manufacturing resources and capabilities has become possible through the centralized management and control of CMfg platform. In fact, the CMfg platform executes the requesters’ tasks by intelligently decomposing the tasks, retrieving functionally similar resources and invoking and composing the services to achieve the optimal condition. The later step, i.e., service aggregation and optimal selection, has become one of the most challenging topics in the field. So, in this paper, after explaining the whole process of matching demands and resources in the context of CMfg, service aggregation and selection process is discussed in a detailed manner. Then, research work done to date regarding service aggregation and composition in CMfg has been investigated in detail from different viewpoints such as selection criteria, solving algorithms, correlation consideration, etc. The goal of this article is to provide a brief guideline for researchers who are aiming to do similar studies and assist them towards a better understanding of related research work done to date.

[1]  Xifan Yao,et al.  Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing , 2017 .

[2]  Philip Moore,et al.  Cloud manufacturing – a critical review of recent development and future trends , 2017, Int. J. Comput. Integr. Manuf..

[3]  Fei Tao,et al.  Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .

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

[5]  Fei Tao,et al.  An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing , 2016, Journal of Computing and Information Science in Engineering.

[6]  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..

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

[8]  Yingfeng Zhang,et al.  Task-driven manufacturing cloud service proactive discovery and optimal configuration method , 2016 .

[9]  Xifan Yao,et al.  A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition , 2017 .

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

[11]  Dazhong Wu,et al.  Cloud manufacturing: Strategic vision and state-of-the-art☆ , 2013 .

[12]  Yanlong Cao,et al.  Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set , 2015, The International Journal of Advanced Manufacturing Technology.

[13]  Yao Xi-fan,et al.  Formal Verification of Cloud Manufacturing Service Composition and BPEL Codes Generation Based on Extended Process Calculus , 2014 .

[14]  Liang Guo,et al.  Study on machining service modes and resource selection strategies in cloud manufacturing , 2015 .

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

[16]  Wu He,et al.  A state-of-the-art survey of cloud manufacturing , 2015, Int. J. Comput. Integr. Manuf..

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

[18]  Zili Zhang,et al.  QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups , 2017 .

[19]  Chen Yang,et al.  IoT-enabled dynamic service selection across multiple manufacturing clouds , 2016 .

[20]  Lei Wang,et al.  Distributed manufacturing resource selection strategy in cloud manufacturing , 2018 .

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

[22]  Xifan Yao,et al.  A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition , 2017, Int. J. Prod. Res..

[23]  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..

[24]  Yixiong Feng,et al.  A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system , 2016 .

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

[26]  Li-Nan Zhu,et al.  Service-evaluation-based resource selection for cloud manufacturing , 2016, Concurr. Eng. Res. Appl..

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

[28]  Fei Tao,et al.  A chaos control optimal algorithm for QoS-based service composition selection in cloud manufacturing system , 2014, Enterp. Inf. Syst..

[29]  Gang Ma,et al.  Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems , 2013, Int. J. Comput. Integr. Manuf..

[30]  Wenjun Xu,et al.  An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing , 2016 .

[31]  Hong Liu,et al.  A Cloud Manufacturing Resource Allocation Model Based on Ant Colony Optimization Algorithm , 2015 .

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