Commenced in January 2007
Paper Count: 31795
Using Axiomatic Design for Developing a Framework of Manufacturing Cloud Service Composition in the Equilibrium State
Abstract:One important paradigm of industry 4.0 is Cloud Manufacturing (CM). In CM everything is considered as a service, therefore, the CM platform should consider all service provider's capabilities and tries to integrate services in an equilibrium state. This research develops a framework for implementing manufacturing cloud service composition in the equilibrium state. The developed framework using well-known tools called axiomatic design (AD) and game theory. The research has investigated the factors for forming equilibrium for measures of the manufacturing cloud service composition. Functional requirements (FRs) represent the measures of manufacturing cloud service composition in the equilibrium state. These FRs satisfied by related Design Parameters (DPs). The FRs and DPs are defined by considering the game theory, QoS, consumer needs, parallel and cooperative services. Ultimately, four FRs and DPs represent the framework. To insure the validity of the framework, the authors have used the first AD’s independent axiom. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 422
 Thames, L. and D. Schaefer, Software-defined cloud manufacturing for Industry 4.0. Procedia CIRP, 2016. 52: p. 12-17.
 Liu, Y., et al., Cloud manufacturing: key issues and future perspectives. International Journal of Computer Integrated Manufacturing, 2019: p. 1-17.
 Wang, X.V. and X.W. Xu, An interoperable solution for Cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 2013. 29(4): p. 232-247.
 Tao, F., et al., Manufacturing service management in cloud manufacturing: overview and future research directions. Journal of Manufacturing Science and Engineering, 2015. 137(4): p. 040912.
 Wang, X., S. Ong, and A. Nee, A comprehensive survey of ubiquitous manufacturing research. International Journal of Production Research, 2017: p. 1-25.
 Jula, A., E. Sundararajan, and Z. Othman, Cloud computing service composition: A systematic literature review. Expert Systems with Applications, 2014. 41(8): p. 3809-3824.
 Zeng, W., Y. Zhao, and J. Zeng. Cloud service and service selection algorithm research. in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation. 2009. ACM.
 Rauch, E., D.T. Matt, and P. Dallasega, Application of axiomatic design in manufacturing system design: a literature review. Procedia CIRP, 2016. 53: p. 1-7.
 Gonçalves-Coelho, A.M. and A.J. Mourao, Axiomatic design as support for decision-making in a design for manufacturing context: A case study. International journal of production economics, 2007. 109(1-2): p. 81-89.
 Xu, X., From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing, 2012. 28(1): p. 75-86.
 Wang, X.V. and X.W. Xu, ICMS: a cloud-based manufacturing system, in Cloud manufacturing. 2013, Springer. p. 1-22.
 Wu, D., D. Schaefer, and D.W. Rosen. Cloud-based design and manufacturing systems: a social network analysis. in DS 75-7: Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol. 7: Human Behaviour in Design, Seoul, Korea, 19-22.08. 2013. 2013.
 Valilai, O.F. and M. Houshmand, A manufacturing ontology model to enable data integration services in cloud manufacturing using axiomatic design theory, in Cloud-Based Design and Manufacturing (CBDM). 2014, Springer. p. 179-206.
 Delaram, J. and O. Fatahi Valilai, An architectural solution for virtual computer integrated manufacturing systems using ISO standards. Scientia Iranica, 2018.
 Fatahi Valilai, O. and M. Houshmand, A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm. Robotics and computer-integrated manufacturing, 2013. 29(1): p. 110-127.
 Wang, X.V., et al., Ubiquitous manufacturing system based on Cloud: A robotics application. Robotics and Computer-Integrated Manufacturing, 2017. 45: p. 116-125.
 Liu, Z.Z., et al., An Approach for Multipath Cloud Manufacturing Services Dynamic Composition. International Journal of Intelligent Systems, 2017. 32(4): p. 371-393.
 Liu, B. and Z. Zhang, QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups. The International Journal of Advanced Manufacturing Technology, 2017. 88(9-12): p. 2757-2771.
 Zhang, S. and X. Hu, Game analysis on logistics cloud service discovery and combination. International Journal of u-and e-Service, Science and Technology, 2015. 8(10): p. 193-202.
 Dastjerdi, A.V. and R. Buyya, Compatibility-aware cloud service composition under fuzzy preferences of users. IEEE Transactions on Cloud Computing, 2014. 2(1): p. 1-13.
 Wang, D., et al., QoS and SLA Aware Web Service Composition in Cloud Environment. KSII Transactions on Internet & Information Systems, 2016. 10(12).
 Mokhtar, A. and M. Houshmand, Introducing a roadmap to implement the universal manufacturing platform using axiomatic design theory. International Journal of Manufacturing Research, 2010. 5(2): p. 252-269.
 Lei, Y. and Z. Junxing, Service composition based on multi-agent in the cooperative game. Future Generation Computer Systems, 2017. 68: p. 128-135.
 Li, Y., X. Yao, and J. Zhou, Multi-objective optimization of cloud manufacturing service composition with cloud-entropy enhanced genetic algorithm. Strojniški vestnik-Journal of Mechanical Engineering, 2016. 62(10): p. 577-590.
 Xu, W., et al., An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 2016. 84(1-4): p. 17-28.
 Zheng, H., Y. Feng, and J. Tan, A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. International Journal of Advanced Manufacturing Technology, 2016. 84.
 Liu, Y., et al., An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing. Journal of Computing and Information Science in Engineering, 2016. 16(4): p. 041009.
 Zhou, J. and X. Yao, A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. The International Journal of Advanced Manufacturing Technology, 2017. 88(9-12): p. 3371-3387.
 Zhou, J. and X. Yao, DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 2017. 90(1-4): p. 1085-1103.
 Zhou, J. and X. Yao, Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition. Applied Intelligence, 2017: p. 1-22.
 Zhou, J. and X. Yao, Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 2017: p. 1-19.
 Zhou, J. and X. Yao, Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Applied Soft Computing, 2017. 56: p. 379-397.
 Zhou, J. and X. Yao, A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition. International Journal of Production Research, 2017: p. 1-20.
 Wang, S., et al., Towards network-aware service composition in the cloud. IEEE Transactions on Cloud Computing, 2016.
 Huang, B., et al., Cloud manufacturing service platform for small-and medium-sized enterprises. The International Journal of Advanced Manufacturing Technology, 2013: p. 1-12.
 Putnik, G., et al., Scalability in manufacturing systems design and operation: State-of-the-art and future developments roadmap. CIRP Annals-Manufacturing Technology, 2013. 62(2): p. 751-774.
 Esposito, C., et al., Smart cloud storage service selection based on fuzzy logic, theory of evidence and game theory. IEEE Transactions on computers, 2016. 65(8): p. 2348-2362.
 Huang, J., et al., Converged Network--Cloud Service Composition with End-to-End Performance Guarantee. IEEE Transactions on Cloud Computing, 2015.
 Fontanini, W. and P. Ferreira, A game-theoretic approach for the web services scheduling problem. Expert Systems with Applications, 2014. 41(10): p. 4743-4751.
 Li, H., et al., Geo-information processing service composition for concurrent tasks: A QoS-aware game theory approach. Computers & Geosciences, 2012. 47: p. 46-59.
 Suh, N.P., Complexity in Engineering. CIRP Annals - Manufacturing Technology, 2005. 54(2): p. 46-63
 Delaram, J. and O.F. Valilai, An architectural view to computer integrated manufacturing systems based on Axiomatic Design Theory. Computers in Industry, 2018. 100: p. 96-114.
 Valilai, O.F. and M. Houshmand, A Manufacturing Ontology Model to Enable Data Integration Services in Cloud Manufacturing using Axiomatic Design Theory, in Cloud-Based Design and Manufacturing (CBDM). 2014, Springer. p. 179-206.
 Aghamohammadzadeh, E. and O.F. Valilai, A novel cloud manufacturing service composition platform enabled by Blockchain technology. International Journal of Production Research, 2020: p. 1-19.
 Aghamohammadzadeh, E., M. Malek, and O.F. Valilai, A novel model for optimisation of logistics and manufacturing operation service composition in Cloud manufacturing system focusing on cloud-entropy. International Journal of Production Research, 2019. 6(1): p. 345-363.
 Valizadeh, S., O. Fatahi Valilai, and M. Houshmand, Flexible flow line scheduling considering machine eligibility in a digital dental laboratory. International Journal of Production Research, 2019: p. 1-19.
 Assari, M., J. Delaram, and O.F. Valilai, Mutual manufacturing service selection and routing problem considering customer clustering in Cloud manufacturing. Production & Manufacturing Research, 2018. 6(1): p. 345-363.