Cloud Manufacturing System Research Articles

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.

Tackling temporal-dynamic service composition in cloud manufacturing systems: A tensor factorization-based two-stage approach

Service composition (SC) is a critical matching process in cloud manufacturing (CMfg) mainly to organize geographically-scattered manufacturing services for manufacturing tasks. Currently, most of the existing CMfg SC approaches assume that cloud service data are always static without considering the dynamics in cloud environments. Meanwhile, existing approaches rely heavily on easy-to-get dense historical service data for SC. However, adapting such ideal approaches to fast-changing temporal-dynamic cloud environments possibly leads to unsatisfactory matching results. This paper proposes a hybrid two-stage approach to deal with temporal-dynamic CMfg SC problem by incorporating tensor factorization (TF). The approach has two stages: temporal-dynamic prediction (Stage I) and many-objective SC optimization (Stage II). For Stage I, a non-negative TF based CMfg SC predictor is proposed to obtain average attribute values of candidate services in desired time slots easily and conveniently. The predictor extracts temporal-dynamic QoS values of candidate services into low-dimensional latent factor space and build a low-rank approximation. Based on the predicted service data, Stage II optimizes temporal-dynamic CMfg SC solutions by an improved many-objective evolutionary optimizer, GrEA-X. GrEA-X is reinforced with multiple variation operators and a modified mating pool selection procedure to enhance its exploration ability in a given time limit. Numerical experiments are then carried out to validate the efficacy of the proposed approach. Based on these results, we confirm the superior performance of our approach over other benchmarks.

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.

Evolutionary Analysis of Cloud Manufacturing Platform Service Innovation Based on a Multiagent Game Perspective

With the rapid development of cloud computing, the Internet of Things (IoT) and other new generation information technologies, a new service-oriented networked intelligent manufacturing mode—cloud manufacturing (CMfg), is emerged. In the CMfg paradigm, cloud manufacturing platform service innovation is an effective way to improve the service function, service quality and service efficiency of cloud manufacturing platforms. However, the service innovation of cloud manufacturing platforms is complex system engineering, which needs to consider many participants with independent interests simultaneously. If one party’s behaviour strategy changes, the whole system will be affected. In order to adapt the decision-making management of manufacturing service innovation in cloud manufacturing system, to provide the effective decision-making support for service innovation of cloud manufacturing system in uncertain environment, to explore the interaction mechanism and equilibrium strategy of various stakeholders in the evolution process of cloud manufacturing system. This research constructs a tripartite evolutionary game model among participants based on evolutionary game theory, Analyses the influence of participants’ strategy choices, The Cloud manufacturing platform’s service quality coefficients and users’ preference Coefficients on the equilibrium state stability of cloud manufacturing systems, and uses MATLAB software for numerical simulation. The results show that the evolutionary stability strategy of cloud manufacturing enterprises plays a decisive role in the service innovation of the cloud manufacturing platform. When cloud manufacturing enterprises choose a noncooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is innovation. When cloud manufacturing enterprises choose a cooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is affected by the platform’s service quality coefficient and the user’s preference coefficient, and both factors can promote the evolution of cloud manufacturing platform service innovation. The platform’s service quality coefficient has a threshold value, which depends on the relative size of benefits, costs and user’s preference coefficient generated by the service innovation of the platform. Therefore, in the cloud manufacturing mode, the cloud manufacturing platform must rely on different enterprises to provide their own innovation resources to realize the service innovation of the cloud manufacturing platform, so the cloud manufacturing platform should focus on introducing a group of high-quality cloud manufacturing enterprises for cooperation. In addition, users’ strong demand for high-quality services can provide sufficient impetus for cloud manufacturing enterprises to choose cooperation and cloud manufacturing platforms to choose innovation, which can not only encourage cloud manufacturing platforms to provide personalized services for users but also avoid the extra waste of resources.

A Bi-objective Model for the Cloud Manufacturing Configuration Design with Resilience and Disruption Risks

Given the fourth industrial revolution (i.e., Industry 4.0), a cloud manufacturing (CMfg) system is introduced as a modern service- and customer-centered manufacturing paradigm. This system incorporates the distributed manufacturing corporations to collaborate as an interconnected system and share their production capabilities or resources without any stoppages. In this regard, this paper develops a novel two-stage bi-objective mathematical model for designing resilient CMfg configuration by maximizing the platform's profit and maximizing the resilience level of the designed CMfg network. Three primary resilience indicators, including design quality, proactive capability, and reactive capability, are considered in the proposed model. Moreover, to evaluate the resilience level of the designed CMfg network, a new objective function is developed. An ε-constraint augmented (AUGMECON2) method is used to solve the bi-objective model. Furthermore, a simple maximum-likelihood sampling (MLS) method is used to reduce the number of possible scenarios. Finally, some in-depth analyses are carried out to illustrate and validate the performance of the proposed solution approach.

Blockchain-based cloud manufacturing platforms: A novel idea for service composition in XaaS paradigm.

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.

Topology analysis of manufacturing service supply–demand hyper-network considering QoS properties in the cloud manufacturing system

• Cloud Manufacturing (CM) integrates distributed resources in the form of virtualized objects • The literature shows the service matching problem is only considered in operational perspective • A graph-based analysis of QoS properties in hyper-network-based model of manufacturing services is proposed. • The focus is to give insight for tactical/strategic planning of CM especially for engaging SME business models. • An insight for modeling the service demand matching problem capable of analyzing the complexity in CM is achieved. Cloud Manufacturing (CMfg), a new manufacturing paradigm, enables an opportunity for manufacturing enterprises to respond to a wide variety of demands. The CMfg concept integrates the distributed resources and capacities in a shared economy, transfers them to the virtual manufacturing services with various quality of service (QoS) properties. These services are invoked to collaborate in producing customized demands with specific functional and non-functional properties. The operational perspective of the service-demand matching problem is considered more in the literature; however, it is a challenging problem in the CMfg ecosystem. This paper evaluates the service-demand matching problem from a structural perspective to facilitate resilience and configurable system working well in the operational interactions. Therefore, a CMfg hyper-network-based model is proposed which is enriched with QoS properties. It employs the available capacities in all small and medium enterprises (SMEs) and large companies. The service-demand matching problem is a complex issue; so, it is impossible to analyze it with the current approaches. Here, the CMfg structure is examined through topological characteristics. It helps decision-makers to provide a robust and resilient service-demand matching model. It also proposes some investment opportunities for the SME. The proposed model and its capabilities are discussed through a real case study about the COVID-19 ventilator manufacturing systems in CMfg ecosystem.

Allocation and scheduling of digital dentistry services in a dental cloud manufacturing system

ABSTRACT Recent enhancements in computer aided design and manufacturing have made dentistry workflow more effective. Dental products are highly customized and personalized for patients. So, they require manufacturing processes for flexibility and agility and fulfilling requirements like quality, delivery time and cost. Various types of scanners, design software and production devices are used by stakeholders in digital dentistry. Also, other stakeholders such as dentists, dental surgeons and lab technicians work together to respond to patient demands to produce the required products while they are distributed in different geographical locations. This paper introduces a new practical and multidisciplinary contribution named as personalized dental production system. The system uses the cloud-based manufacturing paradigm to shape the collaborative workflow of medical manufacturing processes. This workflow is modeled using XaaS (Everything as a Service) approach and addresses limitations such as delivery time and cost of production services. A new mathematical model is developed to allocate and schedule all tasks to providers. The developed model allocates and schedules tasks to providers considering services composing and orders decomposing simultaneity. To response NP-hard challenges of the mathematical model, a new algorithm is proposed. The results show that the proposed algorithm generates appropriate solutions within a reasonable time.

Single‐objective service composition methods in cloud manufacturing systems: Recent techniques, classification, and future trends

AbstractIn recent years, cloud manufacturing (CMfg) has been developed as an intelligent manufacturing system, in which geographically distributed manufacturing resources are available as services in the cloud platform. Choosing and integrating single services into a combined service to fulfill the client's requests requires higher emphasis. However, by increasing customers' trend to utilize CMfg, service providers are encouraged to publish services with various functional and non‐functional characteristics. Thus, service composition and optimal selection has become one of the most challenging topics in CMfg. Hence, an inclusive review of current studies on this NP‐hard issue is extremely desirable. This article first, selects the recent studies in the field of single‐objective service composition in CMfg and classifies and surveys them comprehensively in terms of QoS parameters, energy consumption, user constraint, and so forth. This article aims to provide a useful roadmap for future researchers who are intended to explore novel work in this field. The search for articles was conducted in November 2020.

Alternative process routing and consolidated production-distribution planning with a destination oriented strategy in cloud manufacturing

ABSTRACT 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.

Classification of research and applications of the computer aided process planning in manufacturing systems

The Computer Aided Process Planning (CAPP) systems are recently developed in manufacturing engineering to provide links between Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) systems. The CAPP systems are developed by considering the different issues of computer applications in production engineering. Optimization techniques can be applied to the CAPP to increase efficiency in part production processes. The energy consumption of part production process can be analyzed and optimized using the CAPP systems in order to increase added value in the part manufacturing process. Also, artificial neural networks as well as cloud manufacturing systems can be applied to the CAPP systems to share advantages of the different CAPP systems in different industry applications. Flexible process planning systems are developed using dynamic CAPP in order to cope with product varieties in process of part production. To develop potential energy saving strategies during product design and process planning stages, the advanced CAPP systems can be used. In this paper, a review of Computer Process Planning systems (CAPP) is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers.

Task Decomposition Based on Cloud Manufacturing Platform

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.

Customer satisfaction service match and service quality-based blockchain cloud manufacturing

Blockchain cloud manufacturing is an emerging service-oriented paradigm that takes many advantages to cloud manufacturing. Previous researchers have studied from eradicating third party trust problem among service providers and customers by decentralized data record and involving smart contract. However, the existing paradigm still suffers from third party trust problem among service-related participants and cloud manufacturing provider since service is solely managed by cloud manufacturing provider without supervision and service related participants cannot know whether composed service provision which indeed is NP-hard problem is optimal. In this paper, the architecture of blockchain cloud manufacturing system is proposed with adoption of several cloud manufacturing providers and vote mechanism to vote for optimal composed service provision on candidate composed service scheme firstly calculated by one cloud manufacturing provider. According to customer satisfaction-based service match and quality control, a system framework of composed service performance evaluation is desifned to incorporate the vote mechanism. Firstly, customer satisfaction dimensions as evaluation dimensions are extracted from customer text reviews based on latent dirichlet allocation (LDA) and sub-dimensions under each customer satisfaction dimension are set up based on professional field knowledge. Secondly, overall customer satisfaction rating of reviews and sentiment of reviews towards customer satisfaction dimensions are analysed by three score scheme, bag of words (BOW) and support vector machine (SVM) classification. Thirdly, the sentiment effect and category of customer satisfaction dimension on overall customer satisfaction are identified with the application of neural network and Kano model. Fourthly, in consideration of category characteristic, overall performance of customer satisfaction dimension is measured as distance and using proposed Logit-SVM method. Service selection standard according to value of customer satisfaction is set up. Finally, one empirical study is presented to verify the feasibility of proposed system framework, and proposed model is further discussed.

Design and development a model to present practices for implementation cloud manufacturing system

The purpose of this paper is to identify and classify the main factors implementing the cloud manufacturing systems in the internet service providers company by fuzzy cognitive map methodology. Through expert opinions, 20 main factors were identified and classified based on the importance and then a fuzzy cognitive map approach was applied for obtaining the relationship between the factors, and all of the impact factors are outputs of expert opinion. The outcomes of the study highlighted that three factors, including customer scoring, R&D, and method development were the most important factors impressing the implementation cloud manufacturing systems. Present practices for implementation cloud manufacturing system is and the relationship between the main factors also impressing cloud manufacturing system by employing the fuzzy cognitive map approach in the Iranian internet service provider company. The model obtained in this study guides the managers to identify and classify the important factors of the cloud manufacturing and finally implement it successfully.

A matching mechanism for public cloud manufacturing platforms using intuitionistic Fuzzy VIKOR and deferred acceptance algorithm

ABSTRACT Online manufacturing platforms are spreading in the manufacturing industry in the shade of Cloud Manufacturing systems. The paper manipulates a matching mechanism for public Cloud Manufacturing platforms based on the Intuitionistic Fuzzy Sets Theory, VIKOR method, and Deferred Acceptance algorithm. The VIKOR method provides a rational, scientific, and systematic approach to enable providers and consumers to evaluate the attributes of each other and develop their preference lists. The method is augmented by the Intuitionistic Fuzzy Sets Theory to grasp the high level of vagueness and uncertainty in the process of preference list development. The utilization of Deferred Acceptance algorithm from matching games augmented the proposed mechanism for presenting stable pairs of manufacturing partners. Also, the paper studies the influence of proposer, matching algorithm, and ranking method on the mechanism. Finally, the paper presents some managerial insights, concludes the results, and states future research directions.

Multi-user-oriented manufacturing service scheduling with an improved NSGA-II approach in the cloud manufacturing system

Manufacturing service scheduling (MSS) is an important step in managing the social resource services in the cloud manufacturing (CMfg) system. However, recent research investigates the problem almost from the task level, and little research considers the demands of multiple users in MSS problem. In this paper, the obvious characteristics of multi-user-oriented MSS are analysed by comparing with the multi-task-oriented MSS problem, and then a multi-user-oriented MSS mathematical model is built to cater to the practical demands of multiple users. In order to solve the proposed model, an improved NSGA-II (INSGA-II), integrating k-means algorithm and local search strategy, is developed to improve the quality of solutions. Six scenarios are given to verify the effectiveness of the proposed algorithm by comparing with other three algorithms from four metrics. The flexibility and universality of the proposed model is examined and the effect of user requirements on the Pareto solution is analysed. The results present the efficiency of k-means cluster and local search in the INSGA-II algorithm and provide a practical solution to select the better schedule for users.

Equilibrial service composition model in Cloud manufacturing (ESCM) based on non-cooperative and cooperative game theory for healthcare service equipping.

Industry 4.0 is the digitalization of the manufacturing systems based on Information and Communication Technologies (ICT) for developing a manufacturing system to gain efficiency and improve productivity. Cloud Manufacturing (CM) is a paradigm of Industry 4.0. Cloud Manufacturing System (CMS) considers anything as a service. The end product is developed based on the service composition in the CMS according to consumers’ needs. Also, composite services are developed based on the interaction of MCS providers from different geographical locations. Therefore, the appropriate Manufacturing Cloud Service (MCS) composition is an important problem based on the real-world conditions in CMS. The game theory studies the mathematical model development based on interactions between MCS providers according to real-world conditions. This research develops an Equilibrial Service Composition Model in Cloud Manufacturing (ESCM) based on game theory. MCS providers and consumers get benefits mutually based on ESCM. MCS providers are players in the game. The payoff function is developed based on a profit function. Also, the game strategies are the levels of Quality of Service (QoS) based on consumers’ needs in ESCM. Firstly, the article develops a composite service based on a non-cooperative game. The Nash equilibrium point demonstrates the QoS value of composite service and the payoff value for the players. Secondly, the article develops a composite service based on a cooperative game. The players participate in coalitions to develop the composite service based on formal cooperation. The grand coalition demonstrates the QoS value of composite service and the payoff value for the players in the cooperative game. The research has compared the games’ results. The players’ payoff and the QoS value are better in the cooperative game than in the non-cooperative game. Therefore, the MCS providers and consumers are satisfied mutually in the cooperative game based on ESCM. Finally, the article has applied ESCM in a Healthcare Service to equip 24 hospitals in the best time.

Cyber-Physical Cloud Manufacturing Systems With Digital Twins

The emergence of highly productive cyberphysical manufacturing systems has motivated researchers to focus on minimizing communication overheads and saving computing resources. Traditional methods for building virtual machines using web-service and sensor techniques require a lot of system resources and incur high overheads, which make these techniques unsuitable for cyberphysical cloud manufacturing (CPCM) systems. We propose a novel model, which builds digital twins (virtual machines) so that they can be adapted to the CPCM platform. To achieve this goal, we have designed a framework that can incorporate with MTconnect (a manufacturing protocol to retrieve process information from machine tools) to extract data from physical machines. In addition, we have also designed information models to represent the structure and reflect the real-time state of physical machines.

Proof of service power: A blockchain consensus for cloud manufacturing

With the gradual promotion and in-depth application of cloud manufacturing platform, some deep-seated problems restricting enterprises to go to the cloud are gradually exposed. Lack of trust and security of cloud manufacturing platforms are the bottleneck problems. How to ensure the credit of both trading sides and reduce the transaction risk and transaction cost are still urgent problems to be solved. In recent years, with the continuous development and maturity of blockchain technology, it has attracted widespread attention in the manufacturing field. Its characteristics of trustlessness, anti-tampering, traceability, and high transparency provide new ideas for the development of cloud manufacturing platforms and solving the above problems. However, the traditional consensus protocol has problems such as token dependence and high energy consumption, which is not suitable for the special scenario of cloud manufacturing with a large number of big deals. This paper proposes a blockchain consensus protocol of “Proof of Service Power” (PoSP) for application scenarios of cloud manufacturing. The consensus mechanism uses historical transaction information accumulated by member nodes in a cloud manufacturing system to calculate the service power of member nodes, which greatly reduces energy consumption in the Proof of Work (PoW) process. An introduction of the credit incentive mechanism makes PoSP consensus protocol get rid of token dependence while providing a quantitative basis for trust evaluation for each participant in the cloud manufacturing system. The experimental results show that the consensus mechanism for proof of service power has a good incentive effect, and it has lower energy consumption and higher transaction per second (TPS) than the proof of work and proof of stake (PoS). This study provides new ideas and a reference pattern for the application of blockchain technology in cloud manufacturing platform.

A utility-aware multi-task scheduling method in cloud manufacturing using extended NSGA-II embedded with game theory

ABSTRACT As an emerging sharing and collaborative paradigm, the cloud manufacturing system should maximize the satisfaction of stakeholders to promote the long-term development of the system. This article proposes a new utility-aware cloud manufacturing multi-task scheduling model, which considers the utilities of both customers and manufacturers. To solve the proposed model, an extended non-dominated sorting genetic algorithm-II with three improvements is presented to find the approximate optimal Pareto solution set. Then, these non-dominated solutions are ranked by means of game theory, and the resulting optimal solution is recommended to the cloud manufacturing system. Simulation experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with three baseline multi-objective evolutionary algorithms.