Cloud Manufacturing Service Platform Research Articles

Research on cloud manufacturing service recommendation based on graph neural network.

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes' vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.

Unrelated parallel machine scheduling with processing cost, machine eligibility and order splitting

Under the background of the emerging of shared manufacturing economics, this work addresses a new production order scheduling problem on unrelated parallel machines. Customer orders with release times are of various types depending on their specific product demands. Unrelated machines may be rented for processing orders via one cloud manufacturing service platform. It occurs a uniform processing cost once any machine is rented (via contract) for a given length time duration to process the orders. Each machine is generally able to process some but not all types of orders, i.e., machine eligibility is considered. Orders can be split into sub-orders and processed on their eligible machines simultaneously. For the objective of minimizing the weighted sum of total completion time of orders and total processing cost of machines being used, we establish a mixed integer linear programming model. We provide a lower bound of the considered problem by constraint relaxation, and present the pseudocode of an approach to obtain the lower bound. CPLEX is employed to generate optimal solutions for small-scale instances. For large-scale instances, two heuristic algorithms named Type-Based Greedy and Improved Differential Evolution are developed. The former algorithm selects a smallest set of machines to process the jobs and makes use of job splitting as much as possible, while the latter one is to acquire a best local optimal solution via finite times random global search in the feasible region. Computational experiments are carried out to demonstrate the efficiency and effectiveness of the proposed heuristics.

Game Strategy Selection of Multiple Participants in Cloud Manufacturing Alliance

Introducing the cloud platform operator (CPO) to manage all kinds of manufacturing services on the cloud service platform, the game strategy selection of the multiple participants in the cloud manufacturing alliance is one of current research hotspots of the cloud manufacturing mode. Based on the game theory, this article elaborates the multi-participants business collaboration process and proposes the game strategy selection framework of the multiple participants in the cloud manufacturing alliance. From the perspectives of the manufacturing resource demander (MRD), the manufacturing resource provider (MRP), and the CPO, the game types are analyzed, the benefit functions are established, the existences of game equilibria are proved, and their corresponding constraint conditions are obtained. Integrating the dominant game strategies of the multiple participants, the dominant combination strategies of the above three game equilibria are proposed and the intersection conditions of Nash equilibrium from the mixed perspective are obtained. A set of experimental data from the practical case is given to prove that the solution space of Nash equilibrium from the mixed perspective is not empty. This research provides the theoretical basis for the landing application of the cloud manufacturing mode and the technical support for the development of the cloud manufacturing service platform. It has the important academic value and the broad application prospects.

Online order scheduling of multi 3D printing tasks based on the additive manufacturing cloud platform

Cloud-based additive manufacturing is one of the important forms of cloud manufacturing service platform, which can match manufacturing resources for multi-task requirements to further improve the utilization of idle resources and achieve cost reductions. This paper proposes a more realistic scenario of online scheduling rather than offline scheduling, in which tasks arrive randomly. According to the scenario, a mixed integer linear programming model of multi 3D printing tasks based on the additive manufacturing cloud platform is constructed, with the goal of minimizing the average cost per volume of material. A heuristic strategy is proposed to solve the problem, and different scale instances are tested to compare the performance. Simulation shows that the proposed algorithm can effectively solve the 3D printing online scheduling problem.

Resource Scarcity-Based Pricing for Cloud Manufacturing Service Platform

Based on the architecture and key technologies of cloud manufacturing (CMfg) are becoming increasingly perfect, It is of great significance for the development and application of CMfg to study the resource pricing under the specific transaction environment of cloud manufacturing service platform (CMSP). Hence, an optimal pricing strategy model adapted to the CMSP environment was proposed in this paper. Firstly, the transaction process of CMSP is analyzed, then the static Bayesian model is selected and improved from three aspects. Finally, through the solution of the model and parameter analysis, the advantages of the model are proved, and the pricing strategy is provided for the platform resource providers.

State prediction and servitisation of manufacturing processing equipment resources in smart cloud manufacturing

For enabling the manufacturing processing equipment resources to intelligently perceive their own operating state, this paper proposes an integrated prediction method, in which by combining the clustering ability of SOM and the classification ability of BP together, an integrated intelligent prediction model with the ability of both qualitative and quantitative analysis is defined and used to realise the accurate prediction of operating state. Next, the service encapsulation specification for the various algorithms and model in this method are given. They are encapsulated as a set of cloud services and then published to the smart cloud manufacturing service platform, so as to enable the virtualised resources combine their own processing ability and the intelligent perception ability of these cloud services together by carrying out service composition. The above process realised the networking, servitisation, and intellectualisation of manufacturing processing equipment resources. Finally, the experimental results demonstrate the effectiveness of the proposed method.

State prediction and servitisation of manufacturing processing equipment resources in smart cloud manufacturing

For enabling the manufacturing processing equipment resources to intelligently perceive their own operating state, this paper proposes an integrated prediction method, in which by combining the clustering ability of SOM and the classification ability of BP together, an integrated intelligent prediction model with the ability of both qualitative and quantitative analysis is defined and used to realise the accurate prediction of operating state. Next, the service encapsulation specification for the various algorithms and model in this method are given. They are encapsulated as a set of cloud services and then published to the smart cloud manufacturing service platform, so as to enable the virtualised resources combine their own processing ability and the intelligent perception ability of these cloud services together by carrying out service composition. The above process realised the networking, servitisation, and intellectualisation of manufacturing processing equipment resources. Finally, the experimental results demonstrate the effectiveness of the proposed method.

Research on optimal selection of services and optimal allocation of resources in cloud manufacturing environment

As a new manufacturing mode in recent years, cloud manufacturing has provided a new method for manufacturing industry from low efficiency to efficient utilization of resources, based on the concept of “manufacturing as a service”. Cloud manufacturing is the product of the integration of high-tech, as information technology, Internet of things and big data. To promote global manufacturing to a healthy, green direction, cloud manufacturing technology converges massive service resources, centralizes utilization, reasonably allocates, and realizes high-level utilization and real-time sharing of manufacturing resources. However, due to the large number, wide scope and complexity of cloud manufacturing resources, and the uncertainty and diversity of cloud manufacturing users, how to achieve optimal deployment of cloud manufacturing resources, it is an urgent problem to meet the different individual needs of users and to improve the efficient sharing of manufacturing resources. Therefore, this paper studies cloud manufacturing service optimization, cloud manufacturing service comprehensive evaluation system and resource optimal allocation, and uses the relevant software tools to design the system function modules and establish a cloud manufacturing service platform system.

Research on the Cloud Service Description Model for Cloud Service Composition in Cloud Manufacturing System

cloud service description is the foundation of cloud service composition in cloud manufacturing paradigm. Through unified encapsulation and description, cloud services in cloud manufacturing service platform can provide effective cloud service information for cloud service composition. This paper puts forward the requirements of cloud service description and modeling in cloud manufacturing paradigm. Based on the general ontology construction method, the core ontology and extensible ontology of cloud manufacturing service are constructed by extending OWL-S. On this basis, a formal description model of cloud manufacturing service is established, which supports the implementation of cloud service description method based on OWL-S for cloud service composition.

Cloud Manufacturing Platform and Architecture Design

Abstract Cloud Manufacturing (CMfg) is an emerging networked manufacturing paradigm and service-oriented manufacturing model, in which distributed manufacturing resources are made available by providers according to consumers’ requirements as on-demand manufacturing services via networks (manufacturing clouds) and cloud manufacturing service platforms. Considering the concept of cloud manufacturing and its operation principle, the paper designs and introduces new proposal of cloud manufacturing service platform architecture, including the following six layers: physical resources layer, soft resources layer, virtual resources layer, services layer, application layer and service-oriented interface layer. After surveying majority of papers introducing architectures of CMfg service platforms, the paper recommends to map both hard as well as soft resources into cloud services from resource layers to virtual resource layer which highlights the core idea of the concept. The paper discusses also the fundamentals of the CMfg paradigm, introduces three groups of actors that participate in a cloud manufacturing system, as well as indicates briefly a typical hierarchy architecture of cloud manufacturing system.

Study on pricing behaviour and capacity allocation of cloud manufacturing service platform

Cloud manufacturing service platform plays a very important role in cloud manufacturing service. In this work, using the business management mode of cloud manufacturing service platform as the research object, the pricing problem in a new market structure under cloud manufacturing mode, which consists of a cloud manufacturing service user, two cloud manufacturing service providers and a monopoly cloud manufacturing service platform manufacturer, was studied. Based on the research of bilateral market theory, the pricing model of cloud manufacturing service platform is designed, and the cloud manufacturing service provider carries on Hotelling game to the service request of cloud manufacturing service consumer. Finally, the distribution result of service capability of cloud manufacturing service platform is obtained. Through solving the model, the optimal pricing strategy of cloud manufacturing service platform when profit is maximized and the optimal allocation result of production capacity of the platform are obtained. Finally, the feasibility of the model is verified by a calculation example. It is also found that the profit of cloud manufacturing service platform is inversely related to the indirect network parameters.

Two-dimensional optimization mechanism and method for on-demand supply of manufacturing cloud service

Abstract The on-demand supply mode of cloud manufacturing is significant in enlivening the manufacturing resources over a wide range. However, problems in coupling and contradiction exist among individuation, cost and response time in the on-demand supply of manufacturing cloud service, thus decreasing the efficiency and effectiveness of cloud manufacturing. To decouple this contradiction problem and help users rapidly obtain individuation services at low cost, this paper proposes a two-dimensional (time and space dimensions) optimization mechanism and method for manufacturing cloud service. In the mechanism layer, the user demand decoupling point principle, which is formulated using the customer order decoupling point in traditional physical products, is proposed for describing the optimization direction; and the on-demand supply mode of manufacturing cloud service is divided into four sub-modes, i.e., provide-to-demand, compose-to-demand, engineer-to-demand and research-to-demand; then, a two-dimensional optimization model is established for describing the optimization mechanism among individuation, cost and response time. In the method layer, the decision-making process model for positioning and optimizing UDDP is studied to support the time dimension optimization of moving UDDP backward; and a recursive mapping model of the service topological structure is proposed for supporting the space dimension optimization of the service topological structure. Finally, the proposed model and method are applied to a cloud manufacturing service platform for the mold industry. Two case studies are presented for demonstrating the feasibility and effectiveness of this two-dimensional optimization approach.

Resource selection method based on service capability in cloud manufacturing

Cloud manufacturing is a new manufacturing model based on the network. It collects unevenly distributed physical manufacturing resources of the world into virtual resources and provides services outward by providing services. When a large number of resources accumulate, it is difficult to select optimal resources objectively and a good resource selection method is needed. Service capability is the attribute of resources to provide external services. It plays an extremely important role in measuring the quality of resources. This paper performs analysis in two contexts: the business system and manufacturing process. It uses workflow log mining technology to provide a favourable environment for resource selection. The paper proposes a resource selection method based on service capability and applies it to experimental analysis of a city's the research and development project named 'cloud manufacturing service platform for small and medium-sized enterprises'. The results show that the method proposed is feasible.

Resource selection method based on service capability in cloud manufacturing

Cloud manufacturing is a new manufacturing model based on the network. It collects unevenly distributed physical manufacturing resources of the world into virtual resources and provides services outward by providing services. When a large number of resources accumulate, it is difficult to select optimal resources objectively and a good resource selection method is needed. Service capability is the attribute of resources to provide external services. It plays an extremely important role in measuring the quality of resources. This paper performs analysis in two contexts: the business system and manufacturing process. It uses workflow log mining technology to provide a favourable environment for resource selection. The paper proposes a resource selection method based on service capability and applies it to experimental analysis of a city's the research and development project named 'cloud manufacturing service platform for small and medium-sized enterprises'. The results show that the method proposed is feasible.

Coordination and Optimization of Large Equipment Complete Service in Cloud Based Manufacturing

The paper studies the cloud manufacturing service platform and mode in the coordination and optimization of large equipment complete service (LECS). A set of theory based system of coordination and optimization is systematically established to support and implement LECS' cloud manufacturing mode. The research results show that the collaborative logic framework proposed is of macro guidance significance; the composite synergy mechanism system designed realizes all-round cooperative target; the collaborative optimization model and algorithm established have validity and practicality through instance verification. It systematically realizes the collaborative management of resource choice and optimizing configuration, the plan and control in the process of service, and so on. It can ensure the stability of manufacturing resource service seamless, green, environmental protection, and high quality. It achieves optimization of the overall system coordination. The study also provides a theoretical basis and scientific method for large equipment enterprise from manufacturer to a service integrator transformation.

Research on Mass Manufacturing Resource Sensory Data Management Based on Hadoop

Aiming at the management issue of mass sensory data from the manufacturing resources in cloud manufacturing, a management method for mass sensory data based on Hadoop is proposed. Firstly, characteristics of sensory data in cloud manufacturing are analyzed, meanings and advantages of Internet of Things and cloud computing are elaborated. Then the structure of the cloud manufacturing service platform is proposed based on Hadoop, the information model of manufacturing resources in cloud manufacturing is defined, and the data cloud in the cloud manufacturing service platform is designed. The distributed storage of mass sensory data is implemented and a universal distributed computing model of mass sensory data is established based on the characteristics of Hadoop Distributed File System (HDFS).

A system design method for cloud manufacturing application system

Cloud manufacturing (CM) is a new service-oriented networked manufacturing model put forward in 2010. Its obvious features are service-oriented, knowledge-based, and fusion network. The CM as an outcome of multidisciplinary integration has a very complex system structure. It is obvious that the implementation of CM-oriented production mode is system engineering. However, there is lack of guidance method or strategy about how to build specific cloud manufacturing application system (CMAS) from the system engineering view. To this end, the cloud manufacturing system (CMS) is analyzed from six dimensions by means of generalized modeling idea in this paper. An overall frame design method is proposed to build specific CMAS according to the above six dimensions. It is named as system design method for CMAS. In the end, a mechanical CM service platform is designed with this method, and its operation process is given. This case study shows that the system design method is valuable and meaningful for CM system design.

A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics

To improve the efficiency and productivity of modern manufacturing, the requirements for enterprises are discussed. The new emerged technologies such as cloud computing and internet of things are analyzed and the bottlenecks faced by enterprises in manufacturing big data analytics are investigated. Scientific workflow technology as a method to solve the problems is introduced and an architecture of scientific workflow management system based on cloud manufacturing service platform is proposed. The functions of each layer in the architecture are described in detail and implemented with an existing workflow system as a case study. The workflow scheduling algorithm is the key issue of management system, and the related work is reviewed. This paper takes the general problems of existing algorithms as the motivation to propose a novel scheduling algorithm called MP (max percentages) algorithm. The simulation results indicate that the proposed algorithm has performed better than the other five classic algorithms with respect to both the total completion time and load balancing level.

Agent-based manufacturing service discovery method for cloud manufacturing

The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.