Cloud Manufacturing System Research Articles

Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects

Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects

A Review of Game Theory Models to Support Production Planning, Scheduling, Cloud Manufacturing and Sustainable Production Systems

Cyber-physical systems, cloud computing, the Internet of Things, and big data play significant roles in shaping digital and automated landscape manufacturing. However, to fully realize the potential of these technologies and achieve tangible benefits, such as reduced manufacturing lead times, improved product quality, and enhanced organizational performance, new decision support models need development. Game theory offers a promising approach to address multi-objective problems and streamline decision-making processes, thereby reducing computational time. This paper aims to provide a comprehensive and up-to-date systematic review of the literature on the application of game theory models in various areas of digital manufacturing, including production and capacity planning, scheduling, sustainable production systems, and cloud manufacturing. This review identifies key research themes that have been explored and examines the main research gaps that exist within these domains. Furthermore, this paper outlines potential future research directions to inspire both researchers and practitioners to further explore and develop game theory models that can effectively support the digital transformation of manufacturing systems.

Optimization of product design process in cloud manufacturing system based on swarm intelligence

ABSTRACT Currently, manufacturing industries in many countries are grappling with an imbalance in the supply and demand of manufacturing resources, encompassing resource scarcity and idleness, as well as insufficient and surplus manufacturing capabilities. To address this issue, cloud manufacturing has emerged, integrating the Internet of Things, cloud computing, and advanced manufacturing technologies. This fusion enables the on-demand allocation of manufacturing resources and capabilities, facilitating efficient networked production. However, in the realm of the Internet, the challenge lies in efficiently aligning abundant information with a company’s manufacturing objectives to genuinely deliver an “on-demand service.” This represents a pivotal issue that demands immediate resolution within the operational framework of cloud manufacturing platforms. To effectively address the optimization challenge in Manufacturing Cloud Services (MCS), this study leverages two dimensions: the fundamental Quality of Service (QoS) of MCSs and the interrelated QoS among these services. A holistic QoS model for assembling MCSs is constructed. In this model, the timely relevance of fundamental QoS data is ensured through the incorporation of a temporal weighting function, while an enhancement in the precision of optimization is achieved by considering the interconnections between service combinations. The final simulation experiments showcase the effectiveness of the proposed approach in this paper.

A Brief Review of Artificial Intelligence Robotic in Food Industry

Artificial Intelligent Robotic (AI Robotic) is the formal term for an ‘intelligent induced’ robot that can operate with vision (eye) to detect a product defect, or a control operating system to assist robot to differentiate good and reject product during production. A number of cutting-edge manufacturing automation technologies have been developed, including automatic inspection, autonomous robots, additive manufacturing, ubiquitous manufacturing, cloud manufacturing, and cyber-physical systems. Intelligent and automation technologies for worker assistance have been considered feasible. As knowledge-driven industries develop in terms of artificial intelligence and robotic automation, and to upgrade the existing and next generation of workforce, higher and further education is needed to advance a better understanding of robotic automation in manufacturers who face endless challenges such as labor shortages, high turnover labor force, product innovation, quality matters, and many others. This paper provides a general view of food supply chain challenges when faced with labor issue, environmental disaster and production problems. AI Robotic application would be a good source of problem solving in food manufacturing, food processing and food handling process.

Scheduling tasks with different structures and arrival times in cloud manufacturing systems by considering combined logistics

Scheduling tasks with different structures and arrival times in cloud manufacturing systems by considering combined logistics

Column generation for service assignment in cloud-based manufacturing

Cloud-based manufacturing has gained significant attention in academia and industry, presenting opportunities for enhanced operational efficiency. This paper addresses two critical challenges in cloud manufacturing: service assignment and transportation within hybrid hub-and-spoke networks. We propose a novel approach to minimize the total cost, encompassing manufacturing and transportation costs, through a mixed integer programming model and a column generation-based algorithm. Numerical experiments are conducted to validate the efficacy of the proposed model and algorithm, considering different system sizes. The results demonstrate the tangible benefits of our approach. By introducing a hybrid hub-and-spoke network, we achieve a substantial average reduction of 7.46% in the total cost of the cloud manufacturing system. Furthermore, our proposed algorithm outperforms existing methods such as Particle Swarm Optimization (PSO) and CPLEX. Particularly, in large-scale instances where CPLEX fails to solve, our algorithm outperforms PSO by an additional 3.43% in terms of cost reduction. Moreover, sensitivity analysis provides valuable insights for cloud manufacturing, contributing to its effective implementation.

Robustness Optimization of Cloud Manufacturing Process under Various Resource Substitution Strategies

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.

Individualized requirement-driven multi-task scheduling in cloud manufacturing using an extended multifactorial evolutionary algorithm

Cloud manufacturing is an emerging manufacturing paradigm, which enables the simultaneous processing of multiple manufacturing tasks based on customer requirements through centralized management and planning of manufacturing services provided by distributed enterprises. How to optimally schedule the multiple manufacturing tasks is an important problem in cloud manufacturing. As cloud manufacturing is a demand-driven manufacturing mode and the requirement of each customer is highly individualized, a new individualized requirement-driven cloud manufacturing multi-task scheduling (IRCMMS) model is proposed in this study. It aims to benefit not only individual customers but also the whole system. To solve the proposed model, an extended multifactorial evolutionary algorithm is designed to obtain the approximate optimal Pareto solution set, which offers more alternatives for the cloud manufacturing system. Experimental results based on different simulation instances confirm the feasibility and effectiveness of the IRCMMS model as well as the efficiency of the algorithm in solving the IRCMMS model.

A similarity-enhanced hybrid group recommendation approach in cloud manufacturing systems

With the development of cloud manufacturing (CMfg), a huge amount of services appears on the Internet, which makes recommender systems in CMfg service a promising research field. To this end, recent studies mainly focus on solving individual recommendation to meet the requirements of every user. However, due to the time complexity problem, ‘Many-to-Many’ recommendation mode is increasing in real applications. To implement such a group recommendation is very challenging, because the system not only needs to achieve high recommendation quality but also satisfies user clusters in an even way. Therefore, we propose a similarity-enhanced hybrid group recommendation approach named HGRA for cloud manufacturing. Specifically, we implement the HGRA system by three main components. Firstly, an enhanced user similarity measuring approach is designed to identify a similar user group based on the non-linear model Proximity-Significance-Singularity (PSS) and Kullback-Leibler (KL) distance algorithms. Secondly, a set of user subgroups are further clustered using K-medoids algorithm, in which additional information similarity is calculated by making full use of functional information about the users. Thirdly, a weighted ranking aggregation model is established to generate a recommendation list according to the representative user of each subgroup. The performance of our system is tested by two data sets from real-world cloud manufacturing systems. The experimental results demonstrate the feasible and effectiveness of our approach, compared with some state-of-the-art benchmark solutions, especially in CMfg systems.

Scheduling and Process Optimization for Blockchain-Enabled Cloud Manufacturing Using Dynamic Selection Evolutionary Algorithm

The blockchain-enabled cloud manufacturing is an emerging service-oriented paradigm, and the scheduling and process optimization for blockchain-enabled cloud manufacturing (SPO-BCMfg) are crucial to achieving the service-oriented goal. The blockchain-enabled cloud manufacturing paradigm improves the collaboration capabilities and information security over the ordinary cloud manufacturing while incorporating distributed storage, consensus mechanism, and cloud-edge collaboration. The above characteristics make SPO-BCMfg a multiobjective scheduling optimization problem. This article establishes the multiobjective SPO-BCMfg model based on a dynamic selection evolutionary algorithm to address the problem. First, we carry out the architecture and the modeling of the blockchain cloud manufacturing system. Then, a novel dynamic selection evolutionary algorithm is proposed, which is used to schedule and optimize the model for the process. In the stage of evolution, the algorithm uses a diversity-based population partitioning technique that utilizes the dynamic distance to realize the selection of elite solutions. The method was experimented on the SPO-BCMfg problem facing five and eight objectives. The experimental results show that the algorithm has a strong processing capacity in terms of convergence and diversity compared with the other advanced evolutionary algorithms.

Integration of sub-task scheduling and logistics in cloud manufacturing systems under setup time and different task arrival times

ABSTRACT With the development of new technologies, cloud manufacturing system allows users to request different services by providing a customer-oriented structure with the possibility of access to distributed manufacturing resources. By departing from literature, this study relaxes the unreasonable assumption that all tasks are available at the first moment of scheduling. In addition, two factors are considered to get the model closer to reality: (1) The time/cost of sub-tasks transfer among different services of enterprises in diverse geographical locations; and (2) Service setup time/cost to perform diverse sub-tasks on a given service consecutively. Taking into account these characteristics, a model including three objects of cost imposed on the cloud manufacturing system, task completion time and service quality is developed. The results confirm the importance of considering task arrival time, logistics time/cost and setup time/cost to get more realistic solutions. GAMS software is employed to solve the problems with small and medium scales while a solution approach method based on a genetic algorithm is developed to solve the problems on a large scale. And finally, a sensitivity analysis is undertaken to gain insight into the impact of time, cost and user’s requirements on the final solution.

Cybersecurity Status Assessment of Cloud Manufacturing Systems Based on Semiquantitative Information

The network security status assessment (NSSA) method can evaluate the network security status of cloud manufacturing systems (CMSs), which is of great significance to reduce the network security risk and loss of CMSs. At present, the NSSA of CMSs suitable for semiquantitative and uncertain information conditions is commonly used, which has certain limitations and low accuracy. This paper proposes an NSSA method for CMSs based on semiquantitative information. First, through the detailed analysis of the influencing factors of the network security status of the CMSs, the security evaluation indicators were selected, and the evaluation framework containing multilevel indicators was established by combining semiquantitative information. Second, the evaluation level was established according to the data distribution and the properties of the indicators. Third, a process of data fusion reasoning based on an evidential reasoning algorithm is designed, and a strong reference fusion case is given. Finally, the effectiveness of the proposed security state assessment algorithm in the NSSA of CMSs was verified and analyzed by simulation experiments. The experimental results show that the proposed method can make full use of semiquantitative information and uncertain information to evaluate the network security status of CMSs, and the evaluation results can reflect the actual security status of CMSs.

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

Cloud manufacturing systems (CMSs) are networked, distributed and loosely coupled, so they face great uncertainty and risk. This paper combines the complex network model with multi-agent simulation in a novel approach to the robustness analysis of CMSs. Different evaluation metrics are chosen for the two models, and three different robustness attack strategies are proposed. To verify the effectiveness of the proposed method, a case study is then conducted on a cloud manufacturing project of a new energy vehicle. The results show that both the structural and process-based robustness of the system are lowest under the betweenness-based failure mode, indicating that resource nodes with large betweenness are most important to the robustness of the project. Therefore, the cloud manufacturing platform should focus on monitoring and managing these resources so that they can provide stable services. Under the individual server failure mode, system robustness varies greatly depending on the failure behavior of the service provider: Among the five service providers (S1-S5) given in the experimental group, the failure of Server 1 leads to a sharp decline in robustness, while the failure of Server 2 has little impact. This indicates that the CMS can protect its robustness by identifying key servers and strengthening its supervision of them to prevent them from exiting the platform.

LM-CNN: A Cloud-Edge Collaborative Method for Adaptive Fault Diagnosis With Label Sampling Space Enlarging

In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method.

A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

In the cloud manufacturing process, service composition can combine a single service into a complex service to meet the task requirements. An efficient service composition strategy is crucial, as it affects the efficiency of resource and capacity sharing in the cloud manufacturing system. However, in the face of a large-scale environment, the existing methods have the problems of slow convergence and instability. To solve the problems above, we propose an improved optimization method, named improved-TC. Specifically, we are inspired by the horizontal crossover of CSO in the hybrid-TC teaching phase, the Hybrid-TC is proposed in our previous work, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). Improved-TC is an improvement on the learning phase of hybrid-TC algorithm, we change the search method of hybrid-TC in the learning phase to a one-dimensional search, thereby some dimensions in the population that are trapped in the local optimum have the chance to jump out of the iteration. Experiments show that our proposed method has a faster convergence speed and more stability in the face of service composition in large-scale environments.

A Comprehensive Optimization Approach Based on Cloud Computing for Logistic Sharing System Planning

Due to the global size of the production space and a large number of suppliers and service recipients, sequencing operations and logistics are very important in achieving optimal production system performance, reducing costs and reducing time. Since, in the cloud manufacturing space, each of the production activities of a product can be assigned to different resources in different places, logistics services are needed to communicate between these resources. Thus, the study aims to propose a model for a comprehensive optimization approach based on cloud computing for logistic sharing system planning. Given the results, regarding both operational services and logistics services in the cloud manufacturing system, in addition to the optimal allocation of activities and logistics services, it is possible to plan and schedule the sequence of operations and logistics to minimize the total cost of the cloud production system. Studies are also being conducted on sharing logistics systems in the cloud.

An Efficient IIoT Gateway for Cloud–Edge Collaboration in Cloud Manufacturing

The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on the uploading and streaming of related field-level manufacturing data. For example, the realization of manufacturing service composition application should match the manufacturing tasks with distributed manufacturing resources according to their working state data and performance measurement data. Therefore, this paper proposes a data integration and analysis framework of a cloud manufacturing system based on cloud–edge collaboration and the Industrial Internet of Things (IIoT). A service-oriented information model is established to uniformly describe the related operational data and functional attributes of heterogeneous manufacturing resources. Secondly, a real-time transmission and integration method of high-volume operational field and sensor data based on message middleware is proposed to realize the remote monitoring of distributed manufacturing resources and efficient distribution of related data. Finally, a cloud–edge collaboration mechanism is put forward to train and update the parameters of various artificial intelligence models deployed at edge gateways. In the experiment, taking the computer numerical control (CNC) lathe as an example, the effectiveness of the proposed manufacturing resource access method is verified. Taking the fault diagnosis model of the CNC lathe as an example, the efficiency of the proposed cloud–edge collaboration mechanism for model updating is verified.

Multistage Cloud-Service Matching and Optimization Based on Hierarchical Decomposition of Design Tasks

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.

System and Risk Analysis of Cloud-Manufacturing System

The Cloud manufacturing(C-Mfg) system involves remote distributed manufacturing resources and capabilities collaborating as a single virtual entity. The system receives orders for custom products and provides manufacturing as a service to the end customers. The primary activities of the system, among others, are transaction control, resource allocation and monitoring, distributed manufacturing, quality control, and delivery of consignments to the customers. The actual manufacturing is done by a network of third-party partner manufacturers providing the services to end customers, helping them with cost reduction, shorter time-to-market, and enabling efficient collaboration between manufacturers in the CMfg network. This paper aims to deep dive into C-Mfg as a system and perform a risk analysis to assess and mitigate the risks associated with the system. While cybersecurity and data security-related concerns are deemed the most serious and need immediate management for the system to succeed, the control of data, compliance with manufacturing standards and guidelines, and lack of accountability and ownership are other serious risks that need significant attention. Identifying these factors will help channel the focus on the management and resolution of these risks to establish digital trust in the C-Mfg system leading to the system’s success.

Integrated configuration design and capacity planning in a dynamic cloud manufacturing system

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers.