Cloud Manufacturing Service Research Articles

A Two-Sided Stable Matching Model of Cloud Manufacturing Tasks and Services considering the Nonlinear Relationship between Satisfaction and Expectations

To improve the satisfaction of both service demanders (SDs) and service providers (SPs) in the matching of cloud manufacturing (CMfg) tasks and services, a two-sided stable matching model of CMfg tasks and service considering the nonlinear relationship between satisfaction and expectations is proposed. As the expectations of SDs and SPs are difficult to be quantified directly, an evaluation method based on interval-valued hesitant fuzzy linguistic sets (IVHFLSs) is first presented. Next, a nonlinear model of satisfaction and expectations is built to quantify the satisfaction, which achieves accurate quantification of satisfaction. Then, a two-sided stable matching model of CMfg tasks and service is built, which takes the satisfaction of SDs and SPs as the optimization goals and considers the individual rationalities and blocking pairs. Finally, an adaptive genetic algorithm (AGA) is designed to solve the proposed two-sided matching model. A practical application and comparison analysis is used to verify the effectiveness and superiority of the research.

Many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

Cloud manufacturing service selection and scheduling (CMSSS) problem has obtained wide attentions in recent years. However, most existing methods describe this problem as single-, bi-, or tri-objective models. Little work deals with this problem in four or more objectives simultaneously. This paper investigated CMSSS problem in consideration of the interests of users, cloud platform and service providers. An eight-objective CMSSS optimization model is constructed for the problem. Meanwhile, a many-objective evolutionary algorithm with adaptive environment selection (MaOEA-AES) is designed to address the problem. Specifically, diversity-based population partition technology is used to divide the population into multiple subregions to maintain the population diversity, and an adaptive penalty boundary intersection (APBI) distance is designed to select elitist solutions in different stages of evolutionary process. The proposed algorithm is tested on 2 cases with 5 and 8 objectives in CMSSS problems and each of them has sixteen experimental groups with different problem scales. The experiment results show that MaOEA-AES is competitive to resolve the MaO-CMSSS model compared with eight state-of-the-art evolutionary algorithms in convergence and diversity.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

A probabilistic-based extendable quantitative evaluation method for 3D printing service in cloud manufacturing

By enabling consumer products to be produced on demand and eliminating waste caused by excessive production and transportation, 3D Printing Cloud Services (3DPCSs) are increasingly welcomed by non-professional customers. With more and more 3D printers becoming available on various 3DPCS platforms, the evaluation and selection problem of 3DPCS has attracted much attention for both novices and experienced users. In this paper, we propose a probabilistic-based extendable quantitative evaluation method for 3DPCS evaluation. This method combines the advantages of the information transformation technique, the multinomial distribution probabilistic model, and the uncertainty based weighting method. Evaluation factors, the major attributes that significantly affect the performance of a 3DPCS, are modeled using probabilistic models. At the same time, historical service data is introduced to dynamically identify and update the evaluation factors. Based on these parameters, the proposed quantitative evaluation method can support the evaluation and comparison of 3DPCSs. Numerical simulation experiments are designed and implemented. The corresponding results verify the effectiveness of the proposed evaluation model. The presented evaluation method can serve as the basis of service evaluation and selection on a 3DPCS platform. Although the focus of this work is on 3DPCS, the idea can apply to other types of cloud manufacturing services.

Comprehensive Evaluation of Cloud Manufacturing Service Based on Fuzzy Theory

Comprehensive evaluation is the key to ensuring that cloud manufacturing services can run smoothly over the whole life cycle of products. It is therefore of great importance to carry out a careful scientific evaluation of cloud manufacturing services. In this paper, a combination of qualitative and quantitative methods was adopted based on fuzzy comprehensive evaluation (FCE), and a comprehensive evaluation index system and fuzzy trapezoidal membership function were established. At the same time, using the analytic hierarchy process (AHP) and entropy method, combination weights were obtained, and finally, a comprehensive evaluation model was determined using the five grades of “excellent”, “good”, “medium”, “qualified” and “poor”. Using 30 examples, it was verified that the established evaluation model could classify cloud manufacturing services effectively and systematically according to the different needs of users, thus providing a more effective reference for cloud manufacturing services.

Collaboration of Cloud Manufacturing Service Based on Service Choreography

Collaboration of Cloud Manufacturing Service Based on Service Choreography

Research on evaluation of manufacturing cloud service oriented to environmental benefits of supply chain.

From the perspective of improving the environmental benefits of the supply chain, in this paper, the evaluation index system of manufacturing cloud service is established. It is oriented to the environmental benefits of the supply chain and takes into account the environmental indicators of the candidate providers. The fuzzy DEMATEL modified entropy method is used to calculate the comprehensive weight of the evaluation index. Then, the candidate manufacturing cloud service providers are selected based on gray relational analysis to ensure the rationality of the evaluation results. The evaluation system not only objectively considers the situation of each supplier but also reflects the platform's emphasis on low-carbon environmental factors. Finally, this paper takes the seat suppliers' selection problem in the supply chain of Tianjin industrial cloud automotive mold supply chain as examples to verify the practical significance of this model. At the same time, this paper can provide ideas for the application of cloud manufacturing mode and the solution of supply chain environment problems.

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.

An effective dynamic service composition reconfiguration approach when service exceptions occur in real-life cloud manufacturing

• A dynamic service composition reconfiguration model when service exceptions occur under practical constraints(DSCRWECPC) in real-life CMfg is established. • DSCRWECPC considers service exceptions, the strict completion time constraint of original CMSC, service occupancy time constraint, and the impact on service quality due to the reconfiguration. • An improved HHO(SCRIHHO) is developed through well-designed strategies aiming at the nature of the DSCRWECPC. • Results of numerical experiments and practical applications verify SCRIHHO is superior to PSO and GWO in tackling the real-world DSCRWECPC. Cloud Manufacturing Service Composition (CMSC), as one of the key issues of Cloud Manufacturing (CMfg), has already attracted much attention. Existing researches on CMSC mainly focus on the optimization efficiency in ideal conditions, while scarcely focus on how to efficiently reconfigure CMSC when service exceptions occur. Uncertain service exceptions often occur during CMSC's execution in real-life CMfg. Thus, it is an urgent issue to perform an adjustment for CMSC to continue to complete the processing task. Besides, some practical constraints are non-negligible in real-world CMfg. Thus, it is necessary to consider them when reconfiguring CMSC. To bridge these gaps, this paper proposes a dynamic service composition reconfiguration model when service exceptions occur under practical constraints (DSCRWECPC). This model redefines optimization objectives, including machining quality, service quality, and cost. Besides, DSCRWECPC considers service exceptions, the cloud manufacturing service occupancy time constraint, the strict time constraint of original CMSC, and dynamic service quality change as its practical constraints. To solve this model, this paper proposes a service composition reconfiguration algorithm (SCRIHHO) based on the strengthened Harris Hawks Optimizer (HHO). Finally, to certify SCRIHHO's performance, this paper conducts numerical experiments and the case application to perform comparisons between SCRIHHO and other algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO)). Results showed SCRIHHO in this paper is superior to PSO, GWO when tackling the practical DSCRWECPC in CMfg.

Cloud manufacturing service composition in IoT applications: a formal verification-based approach

Nowadays, one of the new solutions to develop Internet of Things (IoT) applications is cloud manufacturing. By increasing the number of actuators, industrial devices and sensors in industry 4.0 technology, the IoT is going to improve the intelligence of the enterprise information systems with a fast transition towards flexible, smarter, automated and reactive services. In order to respond the needs more quickly or matching the cloud services with modified requirements, cloud manufacturing service composition is used to take advantage of several services available in the Industrial IoT applications. The cloud-based manufacturing services can be characterized, published, shared, and retrieved over the cloud infrastructure to provide integrated services according to the user QoS requirements in a collaborative environment. In this work, a service composition approach is provided for cloud manufacturing in IoT applications. For evaluating the correctness of the proposed solution, a formal verification method is presented based on Labeled Transition System (LTS). Also, Whale Optimization Algorithm (WOA) is utilized to enhance the Quality of Service (QoS), when the number of cloud services have been increased. Finally, deadlock-free and reachability factors as important logical problems are satisfied in verification of the proposed approach. The verification results obtained under different Linear Temporal Logic (LTL) properties and various number of cloud providers indicate that the proposed approach is a correct and valid solution and it outperforms in terms of the reachability, verification time, deadlock, and memory usage.

A personalized method of cloud manufacturing service customization

ABSTRACT In order to provide consumers with personalized cloud manufacturing services, and to form a crowd-sourcing cloud alliance for Small and Medium enterprises (SMEs), a cloud personalized method based on the Evolutionary Game Algorithm (EGA) was proposed in this paper. First, the cloud services were accurately described from two aspects (the enterprise and resources). Besides, an optimization model was established to obtain cloud alliance which can achieve personalized service customization based on consumer preferences. Furthermore, Evolutionary Game algorithm (EGA) which is skilled at combination optimization, was adopted to solve the proposed model. Finally, experiments were designed to verify the feasibility and efficiency of the method, and the results shown that the proposed method is not only feasible, but has much greater efficiency than existing evolutionary methods.

An indicator and adaptive region division based evolutionary algorithm for many-objective optimization

Problems involving four or more objectives are termed Many-objective problems (MaOPs), which pose serious challenges to existing evolutionary algorithms (EAs). Although EAs via decomposition exhibit encouraging performance in handling MaOPs, they need a set of predefined weight vectors, which is not well adaptable to problems possessing various Pareto front (PF) shapes. Besides, the nature of subproblem formulations renders the overwhelming convergence property. In this paper, we introduce an indicator and adaptive region division based EA, referred to as IREA, which is free from the presetting of weight vectors. To be specific, an angular distance based space division module and a proximity-oriented indicator are incorporated into IREA, where the former highlights diversity adaptively via maximum angular distance while the latter emphasizes convergence in a local manner. Furthermore, the quality of mating pool is leveraged by a coordinate transformation assisted niche technique. The proposed IREA is compared with several prevalent many-objective EAs on scalable MaOPs with varying characteristics, as well as on the many-objective cloud manufacturing service composition problems. The experimental results demonstrate that IREA is highly competitive and can be used as an alternative for handling MaOPs.

Pricing Research on Cloud Manufacturing Service Based on Game Theory

The pricing of the cloud manufacturing service was critical for the success of service transaction. Firstly, based on the hotelling model, this paper discussed the pricing and revenue of providers on the cloud platform for service trading. In the cloud service transaction, the cloud platform is generally the leader of service providers. Therefore, we further proposed the stackelberg game model, in which the pricing of the manufacturing service was determined by the profit ratio of the cloud platform. Thirdly, basing on the utilization rate of the manufacturing service, we adjusted the determined pricing of manufacturing service dynamically to cope with unpredictable service demand. Finally, we compared the price and benefit between the proposed model and the traditional hotelling model. The result shows that the price and revenue of the manufacturing service are improved by introducing cloud platform. In the future, we should consider demanders’ revenue and other factors on the price.

Double Auction-Based Manufacturing Cloud Service Allocation in an Industrial Park

Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —MCSA in CMfg is a complicated and challenging task. The current industrial applications rely on optimization methods, such as all-in-one methods (e.g., genetic algorithm) and multidiscipline design optimization methods (e.g., analytical target cascading), to allocate MCSs. While these methods can help cloud operators obtain feasible allocation solutions rapidly, there exist several disadvantages: 1) they can only solve problems in one-sided settings (one customer with many providers); 2) there is an assumption that the customer’s demand can be provided by any providers, which means providers’ capacities are infinite; and 3) the problem is optimized based on the fixed prices provided by providers, without considering supply and demand. This article presents two multiunit double auction mechanisms that allow multiple customers to trade with multiple providers. Allocation rules can break the assumption and providers’ capacities are limited. MCSs are also priced dynamically based on supply and demand. We show how cloud operators apply two mechanisms first and demonstrate their properties and bidding strategies of customers and providers when the mechanism is not incentive compatible. Finally, our experiments and analysis fully validate the applicability and efficiency of two mechanisms and illustrate how cloud operators can maximize their utilities.

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

A cloud manufacturing service to control outsourcing production

ABSTRACT Outsourcing production enables fabless companies to focus on their main business and yet still stay competitive in the supply chain. However, as the actual production process of a Contract Manufacturer (CM) is encapsulated, a Fabless Manufacturer (FM) frequently struggles with higher inventory levels than its actual needs in response to outsourcing production risks. This study proposes Virtual Production Control Services (VPCS) for a company to identify the production risks of its CM and come up with corresponding solutions for improving demand fulfillment. By mining the updated work-in-process data from the CM, the VPCS can identify the potential production risks in a timely manner by estimating production progress based on an optimal planning model. In addition, production risks can be mitigated after applying a novel risk-control scheme such that demands can be fulfilled with reduced inventory levels. An FM case study indicates that VPCS is a promising method to improve the outsourcing efficiency of a company in the IC supply chain.

Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds

With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model.

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

• Deep reinforcement learning is used to solve the QoS-aware service composition problem in cloud manufacturing with the consideration of logistics. • A DRL algorithm that combines the basic DQN algorithm, the dueling architecture, and the prioritized replay mechanism is proposed. • Experimental results demonstrate the effectiveness, high efficiency, adaptability, and scalability of the proposed DRL algorithm. Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep reinforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service composition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prioritized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.

Resource Scarcity-Based Pricing for Cloud Manufacturing Service Platform

Based on the architecture and key technologies of cloud manufacturing (CMfg) are becoming increasingly perfect, It is of great significance for the development and application of CMfg to study the resource pricing under the specific transaction environment of cloud manufacturing service platform (CMSP). Hence, an optimal pricing strategy model adapted to the CMSP environment was proposed in this paper. Firstly, the transaction process of CMSP is analyzed, then the static Bayesian model is selected and improved from three aspects. Finally, through the solution of the model and parameter analysis, the advantages of the model are proved, and the pricing strategy is provided for the platform resource providers.