Research on Cloud Manufacturing Resource Allocation in Distributed Computing Environment

In the cloud manufacturing environment, the sources of manufacturing tasks and resources are more extensive. The balanced utilization of manufacturing resources is conducive to the timely completion of tasks and the good operation of the cloud system platform. Based on the network graph theory, this paper firstly constructed the cloud manufacturing process network graph based on product manufacturing BOM, described the selection constraint relationship between equipment in each process of the product, and formed the matching relationship between product processing process and manufacturing equipment. When new cloud manufacturing tasks and cloud manufacturing resources are added, or the processed and pending tasks and cloud manufacturing resources change, the cloud manufacturing dynamic matching network is updated synchronously. Secondly, considering the load of manufacturing resources, a task load queue centered on manufacturing resources was constructed. The nonlinear programming model was used to build a workshop equipment scheduling model aiming at minimizing the total processing time and cost of products. Finally, genetic algorithm is used to solve and verify the validity and accuracy of the model.

In the cloud manufacturing platform, virtualized manufacturing resources are complex to ensure the authenticity and security of transaction data during the transaction process and rely on the unified deployment of cloud manufacturing platform operators (CPOs). This centralized framework is prone to a single point of failure and risks leakage of private data. Because of these problems, combined with the advantages of decentralization and immutability of blockchain, we innovatively explore the application of blockchain technology in cloud manufacturing and propose a blockchain-based cloud manufacturing resource allocation method. Firstly, a blockchain-based decentralized cloud manufacturing trading platform framework is presented. The Elliptic Curve Digital Signature Algorithm (ECDSA) in manufacturing resource/demand release is studied, and the matching mechanism between manufacturing resources and demand is analyzed. Then, a manufacturing resource verification contract and a manufacturing resource transaction contract for cloud manufacturing are designed using smart contracts (SC). The experimental tests in the Remix platform are completed using Solidity. The test results show that the proposed method can provide a safe and reliable guarantee for virtualized manufacturing resource transactions. Finally, the game problem of supply-demand balance between manufacturing resource suppliers (MRS) and demanders (MRDs) under the decentralized architecture is explored, and simulation is carried out on MATLAB R2021b. The simulation results show the manufacturing resource supply and demand. The fair game can reach the Nash equilibrium and has a faster convergence rate than the existing research.

Cloud manufacturing is defined as a resource sharing paradigm that provides on-demand access to a pool of manufacturing resources and capabilities aimed at utilising geographically dispersed manufacturing resources in a service-oriented manner. These services are deployed via the Industrial Internet of Things and its underlying IT infrastructure, architecture models, as well as data and information exchange protocols and standards. In this context, interoperability has been identified to be a key enabler for implementing such vertically or horizontally integrated cyber-physical systems for production engineering. Previously, the authors proposed an interoperability framework called C-MARS (Cloud Manufacturing Resource Sharing System) to enable error free and non-ambiguous information transfer between the various components and layers of a typical cloud manufacturing system. The main contribution of this paper concerns the identification of the key process parameters for deploying cloud manufacturing processes via a generic and costing-based operation and deployment model (CMARS-ABM) used to simulate interoperable cloud-based manufacturing scenarios for parts of different complexity, varying production numbers, and service composition setups typical to SMEs of varying sizes. Initial results confirm that Cloud Manufacturing is not a one-size-fits-all solution, and that there are indeed a number of driving parameters that need to be analysed to determine whether or not an investment in cloud manufacturing may be financially beneficial and advisable given a specific scenario.

Cloud manufacturing is a new concept extending and adopting the concept of cloud computing for manufacturing. The aim is to transform manufacturing businesses to a new paradigm in that manufacturing capabilities and resources are componentised, integrated and optimised globally. This chapter presents an interoperable manufacturing perspective based on cloud manufacturing. A literature search has been undertaken regarding cloud architecture and technologies that can assist cloud manufacturing. Manufacturing resources and capabilities are discussed in terms of cloud services. A service-oriented, interoperable cloud manufacturing system is introduced. Service methodologies are developed to support two types of cloud users, i.e. customer user and enterprise user, along with standardised data models describing cloud service and relevant features. Two case studies are undertaken to evaluate the reported system. Cloud technology brings into manufacturing industry with a number of benefits such as openness, cost-efficiency, resource sharing and production scalability.

With the development of cloud manufacturing, there is an influx of cloud manufacturing resources on the Internet, which makes it a valuable area of research on how to effectively update the cloud pool of manufacturing resources and improve resource management efficiency in cloud manufacturing. To this end, recent research has focused on addressing the dynamic updating of cloud manufacturing resources to meet the time-sensitive needs of cloud manufacturing. However, due to the increasing diversity of manufacturing resources, the traditional manufacturing resource register only contains static information about manufacturing resources, which can easily have an impact on the dynamic matching and recall of manufacturing resources. So it is very challenging to achieve an update of manufacturing resources, especially video resources. So we propose a dynamic tracking method for spatial features of cloud manufacturing called CMFK (Kalman-based method for dynamic tracking of cloud manufacturing resources). CMFK consists of two main components, firstly the identification, description, and matching of manufacturing resource feature points through computer vision. Secondly, the update and registration of dynamic information are completed. The experimental results demonstrate the feasibility and effectiveness of our approach.

Cloud manufacturing as a new service-oriented business model transforms manufacturing resources into manufacturing services and manages them centrally via Cloud manufacturing platforms. Using distributed manufacturing resources is an opportunity to decrease the logistics and production costs by allocating tasks to manufacturing resources toward task destination nodes. Decomposing ordered tasks to several subtasks, allocating distributed manufacturing resources to tasks, scheduling manufacturing tasks, and select appropriate logistics strategies to deliver tasks to service providers are important activities that must be considered to improve the performance of a Cloud manufacturing system. This paper proposes a mathematical model to improve the performance of the Cloud manufacturing systems. To that end, task decomposition is implemented based on the alternative process routing concept. Decomposing each task to several manufacturing plans results in enhancing system flexibility and consequently improve the performance of cloud manufacturing. Besides, a consolidated production-distribution toward destination nodes strategy is employed to allocate and deliver tasks to manufacturing resources at minimum costs. The impacts of the alternative process routing concept and consolidated production-distribution toward destination nodes strategy are discussed by considering different scenarios and the capabilities of the proposed model have been demonstrated.

Cloud manufacturing is an emerging novel business paradigm for the manufacturing industry. In cloud manufacturing, distributed manufacturing resources are encapsulated into services and aggregated in a cloud manufacturing platform. Through centralized service management, cloud manufacturing is capable of dealing with multiple requirement tasks simultaneously. The ability to deal with multiple tasks at the same time is an important characteristic that distinguishes cloud manufacturing from the previous networked manufacturing models such as manufacturing grid. When it comes to multiple tasks in cloud manufacturing, a critical issue is how to schedule massive services to complete them with shortest makespan, lowest cost, and highest quality, etc. In order to facilitate the research on this issue, we in this paper propose a model for multitask-oriented service composition and scheduling in cloud manufacturing, in which key factures of cloud manufacturing such as service orientation, involvement of logistics, and dynamical change of service availability are taken into account. New concepts such as service efficiency, enterprise capability, and task workload are introduced, and various types of times including service time, logistics time, and waiting time are analyzed in detail. Moreover, this model can be conveniently extended by incorporating new elements such as task constraints, task priority, and continuous task arrival. An example that motivates the current model is presented. Simulation experiments with different numbers of tasks are performed to demonstrate the feasibility of the model.

Cloud Manufacturing (CMfg) combines computing resources of the Internet and manufacturing resources of the enterprise into a large data resource pool to realize the configuration of manufacturing resources and manufacturing tasks. In order to solve the hugeness and complexity of the resource pool in CMfg, simplify the process links and save manufacturing resources, shorten the production time of enterprises and improve the production efficiency of enterprises, a model for the optimal selection of cloud manufacturing resources is proposed, which was based on production cost, production time, processing quality and other evaluation factors.

Due to the individualized consumer needs, cloud manufacturing (CMfg) has been widely used in the optimization of available manufacturing resource allocation to enhance resource utilization and reduce energy consumption. However, efficient scheduling of tasks and subtasks under dynamic CMfg environments to these re-sources are challenging problems. This paper proposes a game theory based on task scheduling and model selection for effectively exploiting distributed manufacturing resources in CMfg, and the Nash equilibrium (NE) in this game theory is implemented by a double ant colony optimization (DACO) algorithm. Through this model, services provided by different providers can handle a batch of tasks in real-time. Besides, to satisfy different service providers and demanders, the proposed approach considers multiple task attributes simultaneously, including completion time, cost, service quality, service composition capability, service availability, energy consumption, service sustainability, service maintainability, and service trust. Simulation results demonstrate that the proposed method is not only effective for the relevant optimization objective but also can achieve great performance under real-time CMfg environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To provide the best production guides, the efficiency of configuration optimization of manufacturing resources is critical to the control and management of smart manufacturing systems. This paper investigates the dynamic scheduling problem for manufacturing services in CMfg. Previous task scheduling approaches fail to evaluate multiple factors together, like completion time, cost, and energy consumption. Also, the traditional scheduling method cannot respond to requests caused by service state changes in an efficient way. Therefore, in this paper, a game theory model that consists of a static scheduling sub-game and a dynamic selection sub-game is presented. This model is achieved by adopting a proposed double ant colony optimization algorithm that solves constrained non-linear programming. Simulation experiments shown in this paper prove that the proposed method outperforms existing scheduling methods in multiple aspects, including completion time and energy consumption. Also, this method can be readily implemented and incorporated into real production environments. Future work can improve the proposed method by analyzing the uncertainty during scheduling tasks and sharing the logistics resources on the same routes.

As a new advanced service-oriented networked manufacturing mode, cloud manufacturing (CMfg) is currently one of the main directions of development in the manufacturing industry. In this study, a resource modeling method oriented to CMfg is proposed to solve manufacturing resource data consistency problems, which are often caused by heterogeneity, diversity, and complexity of manufacturing resource. Motivated by the manufacturing resource modeling literature, we construct a classification model of manufacturing resource using a line indexing method and a manufacturing resource information model for CMfg from the perspective of resource information. The concept hierarchy structure and ontology logical reasoning technology are employed to create this semantic modeling. Based on Ontology Web Language, we propose a new manufacturing resource ontology model that encompasses a resource multilayer model and includes a physical, virtual, and cloud resource data layers, as well as a cloud end layer. We propose a searching and matching mechanism based on semantic similarity degree to locate the manufacturing resource and achieve accurate matching of resource service messages. Such mechanism is an efficient procedure for the manufacturing resource service in the CMfg platform. We present a case study of the proposed framework and model to illustrate validity and practicability of the proposed model.

Dynamic decision-making for knowledge-enabled distributed resource configuration in cloud manufacturing considering stochastic order arrival

Cloud manufacturing is characterized by large uncertainties and disturbances due to its networked, distributed, and loosely coupled features. To target the problem of frequent cloud resource node failure, this paper proposes (1) three resource substitution strategies based on node redundancy and (2) a new robustness analysis method for cloud manufacturing systems based on a combination of the complex network and multi-agent simulation. First, a multi-agent simulation model is constructed, and simulation evaluation indexes are designed to study the robustness of the dynamic cloud manufacturing process (CMP). Second, a complex network model of cloud manufacturing resources is established to analyze the static topological robustness of the cloud manufacturing network. Four types of node failure modes are defined, based on the initial and recomputed topologies. Further, three resource substitution strategies are proposed (i.e., internal replacement, external replacement, and internal–external integration replacement) to enable the normal operation of the system after resource node failure. Third, a case study is conducted for a cloud manufacturing project of a new energy vehicle. The results show that (1) the proposed robustness of service index is effective at describing the variations in CMP robustness, (2) the two node failure modes based on the recalculated topology are more destructive to the robustness of the CMP than the two based on the initial topology, and (3) under all four failure modes, all three resource substitution strategies can improve the robustness of the dynamic CMP to some extent, with the internal–external integration replacement strategy being most effective, followed by the external replacement strategy, and then the internal replacement strategy.

As a service-oriented manufacturing mode, cloud manufacturing has made great progress and technological innovation in recent years by encapsulating manufacturing resources into services. However, the construction of manufacturing resource model basically stays in a static model which lacks authenticity for resources with dynamic properties. This paper establishes the real-time data acquisition and transmission framework of manufacturing resources, and puts forward a dynamic modeling method based on knowledge graph. By building a prototype system of cloud manufacturing platform, the feasibility and effectiveness of data transmission and model construction are verified.

Manufacturing enterprise management mode from the traditional manufacturing production gradually transformed into service-oriented manufacturing in cloud manufacturing. As a carrier for manufacturing resources and manufacturing capabilities, the manufacturer directly affects the advantages and disadvantages of cloud manufacturing services. Therefore, manufacturer optimization has received increasing attention. This paper studies the problem of manufacturer optimization in cloud manufacturing environment, analyzes the 11 kinds of factors which affect the service quality of the manufacturer, meanwhile, it establishes the evaluation indexes system and objective function, optimization algorithm for solving the objective function, and then, it proposes optimization algorithm for solving the objective function, realizes the optimal selection of manufacturers under different demands. The simulation results show that the established evaluation indexes system can display the manufacturing resources and capabilities more fully. Based on the evaluation indexes system model, all the factors that influence the optimization of the manufacturer can be combined together, and the complex optimization problem can be transformed into the mathematical problem. The algorithm can be used to select the best manufacturer under different requirements, and to provide the best service for users.

Cloud manufacturing (CMfg) is a relatively new manufacturing paradigm which promotes easier collaboration among geographically-dispersed manufacturers. Various manufacturing resources can be shared via cloud manufacturing platform as services. The efficacy of such resource sharing and thus collaboration in this service-oriented mode of manufacturing is highly dependent to the efficiency of underlying mechanisms used for processing these services. Activities such as service matching, service retrieval, service composition and optimal selection, and service scheduling are among the most critical of those service processing mechanisms. In this paper, we introduce and implement a comprehensive service composition and optimal selection (SCOS) technique that takes advantage of a real-world manufacturing capability dataset, deep learning models as well as Word2Vec technique to retrieve appropriate candidate sets for each submitted manufacturing subtask in the cloud manufacturing platform. Then, a genetic algorithm is implemented to obtain a near-optimal composite service that achieves the highest Quality of Service (QoS). A series of experiments were conducted to prove the feasibility and efficacy of the approach and results were presented.