Cloud Manufacturing Environment Research Articles

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios.

Improved Chimp Optimization Algorithm for Matching Combinations of Machine Tool Supply and Demand in Cloud Manufacturing

Cloud manufacturing is a current trend in traditional manufacturing enterprises. In this environment, manufacturing resources and manufacturing capabilities are allocated to corresponding services through appropriate scheduling, while research on the production shop floor focuses on realizing a basic cloud manufacturing model. However, the complexity and diversity of tasks in the shop floor supply and demand matching environment can lead to difficulties in finding the optimal solution within a reasonable time period. To address this problem, a basic model for dynamic scheduling and allocation of workshop production resources in a cloud-oriented environment is established, and an improved Chimp optimization algorithm is proposed. To ensure the accuracy of the solution, two key improvements to the ChOA are proposed to solve the problem of efficient and accurate matching combinations of tasks and resources in the cloud manufacturing environment. The experimental results verify the effectiveness and feasibility of the improved ChOA (SDChOA) using a comparative study with various algorithms and show that it can solve the workshop supply and demand matching combination problem and obtain the optimal solution quickly.

A Parallel distributed genetic algorithm using Apache Spark for flexible scheduling of multitasks in a cloud manufacturing environment

ABSTRACT Cloud manufacturing is one of the modern manufacturing systems that aim to control and manage production processes through a cloud platform to optimize the usage of resources and achieve customer objectives. Scheduling large-scale problems with diversity in the required tasks and the available services is a significant challenge for any researcher, especially with the possibility of recurring dynamic events, such as the arrival of new tasks. Thus, our first objective is to present a mixed-integer mathematical model that demonstrates the complexity of scheduling problems in a dynamic CMfg environment. We developed a parallel distributed genetic algorithm (PDGA) for optimizing large-scale scheduling problems on Apache Spark. The PDGA adopts a resilient distributed dataset (RDD) to improve the decomposition of the population and also enhance both the performance and the speed of the evolution process. The PDGA then updates the schedule by using a greedy strategy to improve the overall performance. To verify the effectiveness of the PDGA, we evaluated the algorithm on a benchmark and generated 8 large-scale problems based on this benchmark. The experiments show that the PDGA provides better performance and lower computational time compared to the traditional genetic algorithm and particle swarm optimization algorithm.

Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra’s shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.

Feeding-as-a-Service in a cloud manufacturing environment

The shift towards a mass customization paradigm in production requires the development of new concepts for manufacturing systems. Manufacturing system producers need to address the investment gap between large companies and SMEs to open new market shares and generate new revenue streams. Cloud technologies offer new service models and business opportunities: combined with Product Service Systems ideas, they can have a significant impact on both customers and suppliers. The paper proposes a new concept called Feeding-as-a-Service, which aims to connect servitization and cloud technology to explore how a feeding system can be deployed within an efficient and sustainable Configure-to-Order paradigm in a cloud manufacturing environment. The article outlines the potential system architecture, necessary technologies, and business model for the proposed Feeding-as-a-Service concept and highlights the advantages that the system offers through the enhancement of autonomous robotics capabilities for a cloud-deployed feeding service.

Scheduling method for industrial production processes in cloud manufacturing environment

Scheduling method for industrial production processes in cloud manufacturing environment

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm

This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms.

Optimization of manufacturers based on agent in cloud manufacturing

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.

3D Printing in the Context of Cloud Manufacturing

3D printing has drawn tremendous attention in Industry 4.0. Nowadays, with the ever-increasing consumers’ requests for 3D printing services, there lies a big challenge to eliminate the unbalanced demands and supplies of 3D printing resources in a geographically distributed environment. Cloud manufacturing, as a newly emerged service-oriented manufacturing paradigm, provides a promising way to overcome the challenge by integrating 3D printing into the cloud-based context, which may flourish the development of smart networks of virtual 3D printing cloud and also promote a novel 3D printing service business model to achieve mass customization. This paper presents a comprehensive study of 3D printing in cloud manufacturing environment. Two typical models of 3D printing, i.e. primary 3D printing cloud model and advanced 3D printing cloud model are presented and compared. The system architecture design of a 3D printing cloud platform is proposed to support the latter model. Furthermore, a series of key enabling technologies are presented based on the proposed system architecture. Finally, a prototype of 3D printing cloud platform is deployed with a case study of an aircraft engine part. The research will provide valuable theoretical and practical references to the future development of 3D printing clouds.

Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies.

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.

Service-oriented industrial internet of things gateway for cloud manufacturing

• Proposed a generic system architecture of IIoT-supported cloud manufacturing as a strategic guideline for integrating IIoT technologies in a cloud manufacturing environment. • Proposed a service-oriented PnP IIoT gateway for efficient data acquisition, communication, query, analysis, and visualisation of manufacturing equipment. • Developed two generic service-oriented data schemas for machine tools and 3D printers that allow efficient data management and query for decision-making tasks in a cloud manufacturing platform. • Developed two practical PnP IIoT gateways for a 3D printer and a CNC machine tool that validate the feasibility of the proposed approach. Cloud manufacturing represents a service-oriented manufacturing paradigm that allows ubiquitous and on-demand access to various customisable manufacturing services in the cloud. While a vast amount of research in cloud manufacturing has focused on high-level decision-making tasks, such as service composition and scheduling, the link between field-level manufacturing data and the cloud manufacturing platform has not been well established. Efficient data acquisition, communication, storage, query, and analysis of field-level manufacturing equipment remain a significant challenge that hinders the development of cloud manufacturing systems. Therefore, this paper investigates the implementation of the emerging Industrial Internet of Things (IIoT) technologies in a cloud manufacturing system to address this challenge. We propose a service-oriented plug-and-play (PnP) IIoT gateway solution based on a generic system architecture of IIoT-supported cloud manufacturing system. Service-oriented data schemas for manufacturing equipment are developed to capture just-enough information about field-level manufacturing equipment and allow efficient data storage and query in a cloud time-series database (TSDB). We tested the feasibility and advantages of the proposed approach via the practical implementation of the IIoT gateways on a 3D printer and a machine tool. Our research suggests that purposely developed service-oriented data schemas that capture the essential information for high-level cloud manufacturing decision-making via PnP IIoT technologies are a good solution for connecting field-level manufacturing equipment to a cloud manufacturing platform.

Tripartite Evolutionary Game Analysis of Trust Relationship between Enterprises in a Cloud Manufacturing Environment: A Service Composition Perspective

Due to the existence of limited rationality and information asymmetry, trust between enterprises in the cloud manufacturing environment is characterized by significant complexity. In this paper, to promote the formation of a better trust relationship between enterprises and to ensure the efficient service and stable operation of the manufacturing process to a greater extent, we take into account the subjective cognition of the game parties from the perspective of service combination, introduce the prospect theory, construct an evolutionary game model of “service demander-service provider-platform supervisor,” analyze the evolutionary stabilization strategy, and explore the influence of different factors on the interenterprise trust relationship in the cloud manufacturing environment through numerical simulation. The results show that enhancing the synergy between multiple service providers has a significant impact on improving trust between cloud manufacturing enterprises. The rewards and punishments of the cloud manufacturing platform are conducive to improving interenterprise trust. There exists a critical value dividing opportunistic behavior into explicit and implicit behaviors. When it is less than the critical value, opportunistic behavior is implicit and the service demander trusts the service provider and the transaction continues; when it is greater than the critical value, opportunistic behavior is explicit and the service demander no longer trusts until the service provider is fully trustworthy. The inhibitory effect of good transaction history on opportunistic behavior is evident. Finally, the reasons and effects of the above factors and the subjective perceptions of the game parties in cloud manufacturing are discussed, as well as their concrete manifestations in the CASICloud (China Aerospace Science&Industry Cloud) and relevant suggestions are put forward.

Modeling and simulation of task rescheduling strategy with resource substitution in cloud manufacturing

<abstract> <p>When a cloud manufacturing environment extends to multi-user agent, multi-service agent and multi-regional spaces, the process of manufacturing services faces increased disturbances. When a task exception occurs because of disturbance, it is necessary to quickly reschedule the service task. We propose a multi-agent simulation modeling approach to simulate and evaluate the service process and task rescheduling strategy of cloud manufacturing, with which impact parameters can be achieved through careful study under different system disturbances. First, the simulation evaluation index is designed. In addition to the quality of service index of cloud manufacturing, the adaptive ability of task rescheduling strategy in response to a system disturbance is considered, and the flexibility of cloud manufacturing service index is proposed. Second, considering the substitution of resources, the internal and external transfer strategies of service providers are proposed. Finally, a simulation model of the cloud manufacturing service process of a complex electronic product is constructed by multi-agent simulation, and simulation experiments under multiple dynamic environments are designed to evaluate different task rescheduling strategies. The experimental results indicate that the external transfer strategy of the service provider in this case has higher quality of service and flexibility of service. Sensitivity analysis indicates that the matching rate of substitute resources for internal transfer strategy of service providers and the logistics distance of external transfer strategy of service providers are both sensitive parameters, which have significant impacts on the evaluation indexes.</p> </abstract>

Adaptive multi-objective service composition reconfiguration approach considering dynamic practical constraints in cloud manufacturing

Dynamic uncertainty factors such as equipment faults are common in practically implemented cloud manufacturing (CMfg) environments, often causing the manufacturing service to be invalidated. In that case, efficient reconfiguration of the original service composition under practical constraints is critical; however, existing research scarcely focuses on it. This paper proposes a dynamic service composition reconfiguration model to bridge the gap by considering practical constraints (DSCRPC) in a real-life cloud manufacturing environment. Based on the constraints considered in this study, the DSCRPC model redefines three objectives: time (T*), cost (C*), and product service quality (Q*S*). To optimize the DSCRPC model, this study developed an adaptive multi-population multi-objective whale optimization algorithm (AMPOWOA) based on the Pareto strategy. The algorithm adopts four balancing strategies and adaptively optimizes and adjusts the key parameters under various balancing strategies through well-designed reinforcement learning models. Finally, we conduct numerical experiments and actual application case tests to compare the performances of AMPOWOA and other algorithms (MOWOA, MOHHO, NSGA-II). The results show that DSCRPC can continuously tackle the cloud manufacturing service composition (CMSC) reconfiguration issue with constraints until an order is completed. Moreover, AMPOWOA is superior to the other algorithms optimizing the DSCRPC model. This significantly enhances the robustness of service composition reconfiguration in real-life CMfg.

Matching of Manufacturing Resources in Cloud Manufacturing Environment

With the introduction and application of new information technology in manufacturing, various advanced manufacturing models and national strategies have received more and more attention. The goal of cloud manufacturing is to closely link the resources and capabilities of manufacturers through a variety of services to create a dedicated platform for complex manufacturing process needs. How to achieve effective matching of various manufacturing resources and capabilities in the form of services will be a common problem in the future. In order to effectively improve cloud manufacturing tasks and resource matching efficiency and save resources, this study considers the common aspects of cloud manufacturing resource matching as service quality indicators, and extends the scope to the requirements of manufacturing resources, and the matching pattern of traditional service resources. There are additional restrictions on the resource service matching process. At the same time, the resource service matching is usually asymmetric. Therefore, we introduce the concept of task complexity of demand resources, and propose a combination system based on task complexity and service quality evaluation. The artificial bee colony algorithm (ABC) is used for analysis and verification. The experimental paper further validates the proposed the feasibility and efficiency of the method.

Green Supplier Selection Under Cloud Manufacturing Environment: A Hybrid MCDM Model

In the cloud manufacturing environment, the interaction and cooperation between enterprises become more convenient. The most optimal green supplier selection through cloud manufacturing platform can improve the production quality and sustainable development efficiency of enterprises. However, as alternative suppliers are all over the world, there is a risk that the selected supplier is not ideal. Therefore, in this paper, a hybrid supplier selection model based on TODIM method is proposed to choose the optimal green supplier under cloud manufacturing platform. Considering the characteristics of suppliers in the cloud manufacturing environment and green criteria, the comprehensive green supplier evaluation index system is constructed. The creative application of heterogeneous evaluation information including crisp numbers, interval numbers, and probabilistic linguistic values, can express the evaluation information of different subjects comprehensively. In order to consider human judgment and the importance of information provided by raw data, the criteria weights are determined by integrating fuzzy BWM (Best-worst method) weights and objective entropy weights. Then, with the full consideration of decision maker’s risk attitude, the TODIM method is used to process heterogeneous evaluation information and calculate the priority of the green suppliers. The proposed method is novel, and allows multi-subjects to participate in the assessment process and considers the risk attitude of decision-makers in the green supplier selection process. An empirical study of green supplier selection in the cloud manufacturing environment is conducted. Sensitivity analysis and comparative analysis indicate that the proposed selection model for green supplier is reliable and effective.

Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0

ABSTRACT The number of service offerings in cloud manufacturing continues to grow, so that service level agreement (SLA) within cloud manufacturing is increasingly used to ensure the coordination of the cross-organisational business processes between the cloud service providers (CSP) and cloud service consumers (CSC). The definition of SLA and monitoring SLA’s performance are significant and should be highly valued to ensure the effectiveness and efficiency of business services. At present, the third-party monitoring methods based on service credit are mainly applied in cloud manufacturing environments. There is no effective supervision mechanism to monitor the third party and no efficient punishment mechanism on SLA violation. To address aforementioned issues, this paper proposes a novel Service Level Agreement model by integrating Blockchain and Smart Contract (SLABSC), in which trust issues among CSP, CSC, and the third-party monitoring can be addressed through Blockchain technologies. The method proposed in this paper realises the full functionality of SLABSC and facilitates data security, and it can be used to supervise CSP to provide better services. Experiments results show that the performance of the proposed model is valid and efficient.

Multi-task service composition model considering competitive resource constraint in cloud manufacturing

Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the real cloud manufacturing environment, multi-task service composition is common, so how to formulate a robust multi-task service composition model is a meaningful research direction. This paper first proposes a multi-task service composition optimization model considering resource competition constraints (MTSCOM-RCC) in the cloud manufacturing environment to fill the above-mentioned gap. The MTSCOM-RCC model considers the conflicts of interest and time between parallel subtasks and proposes an important service evaluation indicator. Secondly, a two-layer coding method, including task layer and subtask, is proposed based on studying the MTSCOM-RCC’s competition mechanism and characteristics. An improved hybrid genetic artificial bee colony algorithm model is proposed to solve the multi-task model. The model is combined with the global optimization method and task queue optimization method. Experimental results prove that the two-layer task optimization method model is more feasible and effective than the other two solution models.