Robustness of Cloud Manufacturing System Based on Complex Network and Multi-Agent Simulation

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.

Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

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.

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 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 a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.

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 a new globalized manufacturing framework which has improved the performance of manufacturing systems. The service-oriented architecture as the main idea behind this framework means that all resources and capabilities are considered as services. The agents interact by way of service exchanging, which has been a part of service composition research topics. Service allocations to demanders in a cloud manufacturing system have a dynamic behavior. However, the current research studies on cloud-based service composition are mainly based on centralized global optimization models. Therefore, a distributed deployment and real-time synchronization platform, which enables the globalized collaboration in service composition, is required. This paper proposes a method of using blockchain to solve these issues. Each service composition is considered as a transaction in the blockchain concept. A block includes a set of service compositions and its validity is confirmed by a predefined consensus mechanism. In the suggested platform, the mining role in blockchain is interpreted as an endeavor for proposing the proper service composition in the manufacturing paradigm. The proposed platform has interesting capabilities as it can increase the response time using the blockchain technology and improve the overall optimality of supply-demand matching in cloud manufacturing. The efficiency of the proposed model was evaluated by investigating a service allocation problem in a cloud manufacturing system in four large scale problems. Each problem is examined in four centralized modes, two, three and four solvers in blockchain-based model. The simulation results indicate the high quality of the proposed solution. The proposed solution will lead to at least 15.14% and a maximum of 34.8 percent reduction in costs and 20 to 68.4 percent at the solving time of the problem. It is also observed that with increasing the number of solvers (especially in problems with larger dimensions) the solution speed increases sharply (more than 68% improvement in some problems), which indicates the positive effect of distribution on reducing the problem-solving time.

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC.

Chapter 1. Introduction to scheduling in Industry 4.0 and cloud manufacturing systems.- Chapter 2. Proactive scheduling and reactive real time control in Industry 4.0 .- Chapter 3. Using a digital-twin for production planning and control in Industry 4.0.- Chapter 4. Adaptive Scheduling in the era of Cloud Manufacturing.- Chapter 5. Cloud Material Handling Systems: conceptual model and cloud-based scheduling of handling activities.- Chapter 6. Coupling robust optimization and Model-Checking techniques for robust scheduling in the context of Industry 4.0.- Chapter 7. Integrated scheduling of information services and logistics flows in the omnichannel system.- Chapter 8. Human-oriented assembly line balancing and sequencing model in the Industry 4.0 era.- Chapter 9. A generic decision support tool to planning and assignment problems: Industrial applications & Industry 4.0.- Chapter 10. The Manufacturing Planning and Control system: a journey towards the new perspectives in Industry 4.0 architectures.- Chapter 11. Multi-criteria single batch machine scheduling under time-of-use tariffs.- Chapter 12. Service composition in cloud manufacturing: a DQN-based approach.- Chapter 13. The Tolerance Scheduling Problem in a single machine case.

The 4th Industrial Revolution comprises of SMART factories, SMART manufacturing, Cloud manufacturing and makes use of Internet of Things, Internet of Services and Cyber Physical Systems. The rapid expansion has left shortcomings in defining proper communication protocols between the different components within this industrial revolution. A number of research articles have set out to establish communication protocols between SMART manufacturing units and Cloud manufacturing systems however, there is still not enough to prove for the use of their own protocols within a working case study. A research void exists within this field to establish a foundation to classify the machinery as SMART and to standardize the protocols needed. The purpose of this paper is to review the different communication protocols in the different layers of Cloud manufacturing. This is done by reviewing relevant research done in this field in the recent past. This analysis will give an indication of the progress made in this field and the possible areas for future research.

Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse

ABSTRACTThe purpose of this paper is to construct a causal model of dimensions and their attributes that influence composite service resilience in Cloud manufacturing (CM) system. The composite services are regarded as critical components of CM to accomplish manufacturing jobs and are executed in a distributed, heterogeneous and autonomous environment with high uncertainty and dynamicity. The dimensions and attributes of the proposed model were first identified based on resource-based theory and related literature. Then, the DEMATEL technique was used to measure the strength of influence among the studied factors. The required data were collected through the questionnaires replied by experts from industry and academia. The results of data analysis indicate that virtual resource pool and elastic resource management have the most impact on composite service quality of resilience. This study presents a novel causal model to improve the existing knowledge on composite service resilience in the context of CM. Furthermore, the research findings provide system analysts and designers with a clear definition of composite service resilience. They are useful to design explicit strategies for improving the resilience level of the composite services at different layers of CM architecture in practice.

The background, significance and the related research at home and abroad of software resource re-sharing in cloud manufacturing system are introduced. The current use and system structure of software in manufacturing are analyzed, and they are Web-based, software directly sharing, based package to share software indirectly and server-based technology for common software across the internet remote sharing. The special requirements for software sharing of cloud manufacturing system are presented. According to the feature of small and medium-sized enterprise, a re-sharing approach of cloud manufacturing software sharing scheme for small and medium-sized enterprise is proposed, and this approach is very compatible, extended, and low-cost, it is very suitable for the manufacturing cloud of small and medium-sized enterprise. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.2918

Cloud manufacturing is a new service-oriented intelligent manufacturing paradigm. Knowledge is a core part and the foundation to realize its intelligence. In this paper, the importance and functions of knowledge to cloud manufacturing was first investigated from the lifecycle of cloud service. Then a knowledge management system was designed and the layered architecture and key technologies were analyzed. A case study was conducted to demonstrate the feasibility of the proposed knowledge management system.