Cloud Manufacturing Service Composition Research Articles

Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.

Cloud Logistics Path Optimization Based on Ant Colony Algorithm

Cloud Manufacturing is developed in the cloud computing, which is based on the idea of "Made in service" recently. Cloud manufacturing mode connect all sorts of manufacturing resources and capability together and form standard manufacturing services which customers can obtain according to their demand at any time. Logistics mode should coincide with manufacturing mode for the certain correlation between logistics and manufacturing process,so the production and development of cloud manufacturing mode had the logistics mode reformed. In this paper we present a cloud manufacturing service composition method considering execution reliability based on ant algorithm, to optimize the use of composite service execution path to save the costs.

Research of Cloud Manufacturing Execution Path Optimization Based on Adaptive Ant Colony Algorithm on Hadoop Platform

Cloud Manufacturing is developed in the cloud computing, which is based on the idea of "Made in service" recently. As execution of manufacturing resources is more complicated than computational resources the service combination execution reliability becomes a mandatory considering problem in cloud manufacturing. In this paper we present a cloud manufacturing service composition method considering execution reliability based on adaptive ant algorithm, which in hadoop environment to optimize the use of composite service execution path to save the costs. As the experimental result shown, this method can effectively avoid the congestion of data on the critical path.

Formal Verification of Cloud Manufacturing Service Composition and BPEL Codes Generation Based on Extended Process Calculus

Formal Verification of Cloud Manufacturing Service Composition and BPEL Codes Generation Based on Extended Process Calculus

Service composition in cloud manufacturing based on adaptive mutation particle swarm optimization

To cope with Multi-objective Programming on Manufacturing Cloud Service Composition(MOP-MCSC) problem in cloud manufacturing(CMfg) system,a mathematical model and a solution algorithm were proposed and studied.Firstly,inspired by the resource service composition technology in manufacturing grid,a QoS-aware MOP-MCSC model in CMfg system had been explored and described.Secondly,by analyzing the characteristics of manufacturing cloud services according to the domain knowledge of manufacturing,an eight-dimensional QoS evaluation criterion with corresponding quantitative calculation formulas was defined.Then,the QoS expression of manufacturing cloud service was eventually formulated.Lastly,the MOP-MCSC model was built,and an Adaptive Mutation Particle Swarm Optimization(AMPSO) was designed to realize this model.The simulation experimental results suggest that the proposed algorithm could solve the MOP-MCSC problem efficiently and effectively with a better performance than conventional particle swarm optimization.

Research on the Measurement Method of Flexibility of Resource Service Composition in Cloud Manufacturing

In order to overcome the bottlenecks of traditional network manufacturing, a new service-oriented networked manufacturing model, i.e. the cloud manufacturing (CMfg), was proposed recently. As an effective method for the realization of the added value of manufacturing resource, resource service composition (RSC) plays an important role in the implementation of CMfg. In view of the issue of dynamic changes occurred during RSC in CMfg, this paper presents the concept of flexibility of RSC as well as the idea of optimal-selection of RSC based on flexibility, which can enable RSC to have the ability to adapt to the dynamic changes. Meantime, the measurement method of flexibility of RSC is investigated. The optimal-selection of RSC based on flexibility can be achieved through quantitative evaluation of flexibility.