Optimization technology in cloud manufacturing

Recently, the new generation of information technology as cloud computing, big data, IoT, and AI has led to profound changes in manufacturing. Many new technologies and ideas have emerged in the field of system development, manufacturing process, industrial form, and business model, such as cloud manufacturing (CM). The primary feature of CM is to realize the integration and widely sharing of manufacturing services. Therefore, optimization of manufacturing services is vital for a CM system. Based on wide investigation, this paper summarizes the research progress of CM service optimization in recent years from the aspects of optimization strategy, optimization model, and optimization algorithm. And problems and development trends such as the research of task decomposition, service discovery and optimization integration, algorithm with big data, and optimized object are proposed.

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

[2]  Yuan Cheng,et al.  Common intelligent semantic matching engines of cloud manufacturing service based on OWL-S , 2015, The International Journal of Advanced Manufacturing Technology.

[3]  Fei Tao,et al.  BGM-BLA: A New Algorithm for Dynamic Migration of Virtual Machines in Cloud Computing , 2016, IEEE Transactions on Services Computing.

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

[5]  Xiaorong Huang,et al.  Service requirement conflict resolution based on ant colony optimization in group-enterprises-oriented cloud manufacturing , 2016 .

[6]  C. F. Jian,et al.  BATCH TASK SCHEDULING-ORIENTED OPTIMIZATION MODELLING AND SIMULATION IN CLOUD MANUFACTURING , 2014 .

[7]  Ray Y. Zhong,et al.  Analytical target cascading for optimal configuration of cloud manufacturing services , 2017 .

[8]  Fei Tao,et al.  SDMSim: A manufacturing service supply–demand matching simulator under cloud environment , 2017 .

[9]  Trevor A. Dean,et al.  Application of a continuum damage mechanics (CDM)-based model for predicting formability of warm formed aluminium alloy , 2017 .

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

[11]  Elisabeth J. Umble,et al.  Enterprise resource planning: Implementation procedures and critical success factors , 2003, Eur. J. Oper. Res..

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

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

[14]  Hai Wan,et al.  Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing , 2014, J. Appl. Math..

[15]  William H. Dutton,et al.  Clouds, big data, and smart assets: Ten tech-enabled business trends to watch , 2010 .

[16]  Zhou Zude,et al.  Typical characteristics,technologies and applications of cloud manufacturing , 2012 .

[17]  Zhong Ting,et al.  Optimization model of cloud manufacturing services resource combination for new product development , 2012 .

[18]  Yanlong Cao,et al.  Study on Resource Configuration on Cloud Manufacturing , 2015 .

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

[20]  Pingyu Jiang,et al.  Task-driven e-manufacturing resource configurable model , 2012, J. Intell. Manuf..

[21]  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.

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

[23]  Lida Xu,et al.  Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system , 2013 .

[24]  A. Noorul Haq,et al.  Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach , 2017, J. Intell. Manuf..

[25]  W. B. Heginbotham The valency of change , 1989 .

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

[27]  Liang Guo,et al.  Research on selection strategy of machining equipment in cloud manufacturing , 2014 .

[28]  Yang Chen Clouds capability service of production and processing in cloud manufacturing , 2012 .

[29]  Dexiang Deng,et al.  Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding , 2016 .

[30]  Fei Tao,et al.  Editorial for the special issue on big data and cloud technology for manufacturing , 2016 .

[31]  Chen Gui-song,et al.  Manufacturing resource allocation based on cloud manufacturing , 2012 .

[32]  Guo Wei Quantum harmony search method for design knowledge resource serialization combination in cloud manufacturing environment , 2012 .

[33]  Arnon Rosenthal,et al.  Methodological Review: Cloud computing: A new business paradigm for biomedical information sharing , 2010 .

[34]  Tom Van Woensel,et al.  Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models , 2018 .

[35]  Wu Jian Technical framework for Web Services composition and its progress , 2011 .

[36]  Yuanping Xu,et al.  An integrated solution—KAGFM for mass customization in customer-oriented product design under cloud manufacturing environment , 2016 .

[37]  Xifan Yao,et al.  DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing , 2017 .

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

[39]  Su Kaika Manufacturing resource allocation method based on bi-level programming in cloud manufacturing , 2015 .

[40]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[41]  Harris Wu,et al.  A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing , 2016, Comput. Ind. Eng..

[42]  Jingxiong Qiu,et al.  Combination of cloud manufacturing and 3D printing: research progress and prospect , 2018 .

[43]  Yixiong Feng,et al.  A Hybrid Energy-Aware Resource Allocation Approach in Cloud Manufacturing Environment , 2017, IEEE Access.

[44]  Ray Y. Zhong,et al.  An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services , 2017, Journal of Manufacturing Systems.

[45]  Fei Tao,et al.  FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System , 2013, IEEE Transactions on Industrial Informatics.

[46]  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.

[47]  Duc Truong Pham,et al.  Manufacturing Service Reconfiguration Optimization Using Hybrid Bees Algorithm in Cloud Manufacturing , 2016, Monterey Workshop.

[48]  Pan Yongdong,et al.  Bi-level programming optimization method for cloud manufacturing service composition based on harmony search , 2017, J. Comput. Sci..

[49]  Liang Guo,et al.  A system design method for cloud manufacturing application system , 2016 .

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

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

[52]  Andrew Y. C. Nee,et al.  Advanced manufacturing systems: socialization characteristics and trends , 2015, Journal of Intelligent Manufacturing.

[53]  Fei Tao,et al.  Research on measurement method of resource service composition flexibility in service-oriented manufacturing system , 2012, Int. J. Comput. Integr. Manuf..

[54]  Fei Tao,et al.  A study of optimal allocation of computing resources in cloud manufacturing systems , 2012, The International Journal of Advanced Manufacturing Technology.

[55]  Liang Guo,et al.  Agent-based manufacturing service discovery method for cloud manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[56]  Fei Tao,et al.  Advanced manufacturing systems: supply–demand matching of manufacturing resource based on complex networks and Internet of Things , 2018, Enterp. Inf. Syst..

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

[58]  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..

[59]  Xiaoming Yang,et al.  Additive Manufacturing Cloud via Peer-Robot Collaboration , 2016 .

[60]  Li Cheng-ha Cloud manufacturing service resources based on attribute description matching , 2014 .

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

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

[63]  Fei Tao,et al.  CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System , 2014, IEEE Transactions on Industrial Informatics.

[64]  Ren Lei,et al.  Typical characteristics of cloud manufacturing and several key issues of cloud service composition , 2011 .

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

[66]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[67]  Su Kaika Manufacturing resource allocation method based on non-cooperative game in cloud manufacturing , 2015 .

[68]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[69]  Mark Goh,et al.  Ontological knowledge integration and sharing for collaborative product development , 2018, Int. J. Comput. Integr. Manuf..

[70]  Xing Xu,et al.  The value network optimization research based on the Analytic Hierarchy Process method and the dynamic programming of cloud manufacturing , 2016 .

[71]  Liu Bo Multi-task oriented service composition in cloud manufacturing , 2013 .

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

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

[74]  Jaydip Sen,et al.  Internet of Things - Applications and Challenges in Technology and Standardization , 2011 .

[75]  Shu Hong-ping Active Rent-seeking of Cloud Services Based on Biding Mechanism for Cloud Manufacturing Environment , 2012 .