Cloud Manufacturing Resource Research Articles

An Optimal Selection Method of Cloud Manufacturing Resource for Guide Roller based on Combination Algorithm

An Optimal Selection Method of Cloud Manufacturing Resource for Guide Roller based on Combination Algorithm

Sequential auction for cloud manufacturing resource trading: A deep reinforcement learning approach to the lot-sizing problem

Cloud manufacturing is a rapidly growing trend in modern manufacturing, which has transformed the traditional operations and value chain structure of enterprises. It is crucial to develop a rational and effective trading mechanism of cloud manufacturing resources to meet the ever-increasing demands in this new environment. This paper proposes a sequential auction-based paradigm for the trading of manufacturing resources. The main challenge of the design of the paradigm is to determine the dynamic lot size of resources allocated to each auction considering the uncertainty of arriving demands. To achieve this, we first develop a competitive game model to identify optimal bidding strategies of arrived customers and estimate the expected revenue for each round with the given lot size. Secondly, we construct a Markov decision process (MDP) model to characterize the dynamics of the arrival of stochastic demand and the inventory transition of the manufacturing resources in sequential auctions. Lastly, we leverage a data-driven approach by integrating machine learning with an offline deep reinforcement learning (RL) approach. Specifically, we employ a long short-term memory (LSTM) model to predict forthcoming demands in the environment and develop the deep Q-network (DQN) learning algorithm to optimize lot-sizing policy by interacting with the well-learned LSTM environment model. Our simulation experiments validate the effectiveness of our approach and some management insights are given.

Resource Scheduling Method for Equipment Maintenance Based on Dynamic Pricing Model in Cloud Manufacturing

Cloud manufacturing, as a novel service mode in the manufacturing field with the features of flexible resource assignment, timely service, and quantity-based pricing, has attracted extensive attention in recent years. The cloud manufacturing industry uses a significant amount of smart equipment. In this context, equipment maintenance resource scheduling (EMRS) is an important subject that needs to be studied. Cloud manufacturing platforms must provide effective services for equipment maintenance in a timely manner. In order to improve the efficiency of cloud manufacturing platforms and meet the needs of users, an effective EMRS scheme is required. In this paper, we propose a dynamic resource allocation model for cloud manufacturing to meet the needs of users and maximize the benefit of a cloud manufacturing platform. The model takes into account the needs of users and the benefits of a cloud production platform. The contributions of this paper are divided into the following three aspects. First, the E-CARGO model using role-based collaboration theory is introduced to formally model EMRS activities, forming a solvable optimization model. Second, a dynamic pricing model with a center symmetric curve is designed to realize the flexible conversion between time, cost, and price. Third, the concept of satisfaction in fuzzy mathematics is introduced, in order to meet the different needs of users and platforms, in terms of time, price, and cost, while ensuring service quality and the platform’s benefits. Finally, an improved genetic algorithm is used to solve the cloud manufacturing resource scheduling problem, and good experimental results are obtained. These results demonstrate that the proposed dynamic pricing model is reasonable, and the allocation scheme obtained through a genetic algorithm is feasible and effective.

Optimal selection of cloud manufacturing resources based on bacteria foraging optimization

ABSTRACT As a service-oriented manufacturing paradigm, cloud manufacturing (CMfg) combines the computing resources the manufacturing resources respectively from the Internet and enterprises into a huge shared resource pool for network-based distribution of manufacturing resources and manufacturing tasks. In order to reduce the number and complexity of resource pools in CMfg, it is necessary to simplify the process, save manufacturing resources, shorten the production time and improve the productivity of enterprises. Therefore, a method for the optimal selection of cloud Manufacturing resources based on bacterial foraging optimization was proposed in this paper. First, a scheme evaluation system based on the evaluation factors of production cost, production time and processing quality was established, and a mathematical model of resource optimization selection was built. Secondly, a combination of Analytic Hierarchy Process (AHP) and entropy weight method was used to obtain the weights of different evaluation indicators, and a bacterial foraging optimization algorithm (BFO) was used to optimize the scheme. Finally, an example study of the method was conducted with the NGW51 reducer as an example. By comparing with existing methods, the experimental results verify the advantages of BFO in CMfg resource optimization selection.

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.

Production Task Allocation Decision Based on Cloud Robot Cell-Line.

Cloud manufacturing is a new service-oriented efficient and low-consumption agile manufacturing mode integrating information, manufacturing, Internet of Things, and other technologies. One of the key decisions of production enterprises is production task allocation based on cloud robot cell-line, which determines the efficiency and flexibility of the production system and affects various production links, such as job-shop logistics, production planning, and production scheduling. This paper explores the production task allocation, from the angle of the optimal combination of cloud manufacturing resources. First, a mathematical model was established, based on the transport cost of different sub-tasks and the tardiness cost of product delivery, and solved by quantum firefly algorithm (QFA). Next, QFA was proved superior to traditional firefly algorithm (FA), improved FA, and the FA optimized by cat swarm optimization (CSO-FA), in terms of time complexity and spatial complexity. The research enriches the theory and methodology of allocating operation-level cloud manufacturing resources based on cloud robot cell-line and provides decision support to manufacturers, which want to implement operation-level allocation of cloud manufacturing resources based on cloud robot cell-line.

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

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.

Research on modeling of cloud manufacturing resources selection

Cloud Manufacturing (CMfg) combines computing resources of the Internet and manufacturing resources of the enterprise into a large data resource pool to realize the configuration of manufacturing resources and manufacturing tasks. In order to solve the hugeness and complexity of the resource pool in CMfg, simplify the process links and save manufacturing resources, shorten the production time of enterprises and improve the production efficiency of enterprises, a model for the optimal selection of cloud manufacturing resources is proposed, which was based on production cost, production time, processing quality and other evaluation factors.

A Multidimensional Information Fusion-Based Matching Decision Method for Manufacturing Service Resource

With the development of specialization, coordination and intelligence in the manufacturing service process, the issue of how to quickly extract potential resources or capabilities for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics, have become the critical issues to be addressed in the cloud manufacturing environment. Through the combination of the characteristics of relevance, synergy and diversity of manufacturing service tasks on the intelligent cloud platform, a matching decision method for manufacturing service resources is proposed in this paper based on multidimensional information fusion. On the basis of integrating multidimensional information data in cloud manufacturing resource, the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering (HCF) algorithm. Then, the information of function attribute, reliability and preference is employed to match and push manufacturing service resources or capabilities actively, so as to realize the matching decision of manufacturing service resources with precise quality, stable service and maximum efficiency. At last, a case study of resources matching decision for body & chassis manufacturing service in a new energy automobile enterprise is presented, in which the experimental results show that the proposed approach is more accuracy and effective compared with other different recommendation algorithms.

Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources

Owing to the complexity of socio-economic environments and the fuzziness of human cognition, information of cognitive preference provided by decision-making organizations composed of many experts is often hesitant and fuzzy. In consequence, for the sake of addressing the hesitance and fuzziness of preference information for the configuration of tasks and resources in cloud manufacturing, a decision-making model of a two-sided matching considering a bidirectional projection under preference information of hesitant fuzzy is put forward. Primarily, this paper describes the problem of two-sided matching and introduces the hesitant fuzzy set. Afterwards, according to preference information given by matching agents using the hesitant fuzzy element, the evaluation matrix is constructed. Meanwhile, the bidirectional projection technology and TOPSIS method are combined to calculate the closeness degree matrix. Further, by introducing the constraint of the stable matching, a decision-making model of a two-sided matching for maximizing the closeness degree of two-sided matching agents is constructed, and the optimal configuration results are obtained by the solving of the model. Subsequently, the illustrative case is provided to validate the rationality and effectiveness of the presented model in solving the configuration for cloud manufacturing tasks and resources. Also, the stability in the proposed configuration results is illustrated by making a sensitivity analysis. Further, the reliability in the given solving process is demonstrated through performing a comparative analysis, as well as the advantages in the proposed model is also discussed. It shows that this developed model can give stable configuration results and also provide a matching approach for different agents under an uncertain environment.

Architecture of Cloud 3D Printing Task Modeling for Nodes Dynamic Scheduling and Coupling Based on Complex Networks

To study the variation in resource allocation efficiency in a cloud 3D printing (C3DP) manufacturing grid, C3DP task modeling for a complex network model was established based on the dynamic coupling of nodes, taking cloud manufacturing resource characteristics such as distribution and diversity into account. Based on this idea, we propose an architecture of C3DP task modeling for complex networks based on the dynamic coupling of nodes. First, the model calculates the initial tasks and services by evaluating the quality of the manufacturing resources, and then, according to the strength of the correlations among various tasks and among services and tasks, a multiplex network model based on cloud tasks and services is constructed. Then, according to the description and its constraint structure definition of the C3DP order tasks, by using the intercept method for complex networks, this paper identifies the work breakdown structure of coupling task sets, simplifies the optimization process, and proposes a C3DP order task (C3DPOT) network modeling method. Third, on the basis of the formal description of C3DP service resources, an architecture of C3DP order task methods for complex networks based on the dynamic coupling of nodes is proposed. Finally, a Service_Net model example is designed to verify the feasibility of the method.

Research on optimal selection of services and optimal allocation of resources in cloud manufacturing environment

As a new manufacturing mode in recent years, cloud manufacturing has provided a new method for manufacturing industry from low efficiency to efficient utilization of resources, based on the concept of “manufacturing as a service”. Cloud manufacturing is the product of the integration of high-tech, as information technology, Internet of things and big data. To promote global manufacturing to a healthy, green direction, cloud manufacturing technology converges massive service resources, centralizes utilization, reasonably allocates, and realizes high-level utilization and real-time sharing of manufacturing resources. However, due to the large number, wide scope and complexity of cloud manufacturing resources, and the uncertainty and diversity of cloud manufacturing users, how to achieve optimal deployment of cloud manufacturing resources, it is an urgent problem to meet the different individual needs of users and to improve the efficient sharing of manufacturing resources. Therefore, this paper studies cloud manufacturing service optimization, cloud manufacturing service comprehensive evaluation system and resource optimal allocation, and uses the relevant software tools to design the system function modules and establish a cloud manufacturing service platform system.

Multi-objective scheduling of cloud manufacturing resources through the integration of Cat swarm optimization and Firefly algorithm

Multi-objective scheduling of cloud manufacturing resources through the integration of Cat swarm optimization and Firefly algorithm

The manufacturing resource optimization problem of federal collaborative development based on Markov Decision Process

To solve the problem of optimal allocation of cloud manufacturing resources considering of the uncertainty during the product development process, a Markov decision model for the development of the capability unit assignment is proposed. And the cross-entropy method is adopted to obtain the optimal allocation strategy of the developed capability unit by the learning strategy. The simulation results show that the optimal strategy is better than the stochastic allocation strategy, which can reduce the cost of product development and improve the efficiency of product development.

Ontology for cloud manufacturing based Product Lifecycle Management

The manufacturing environment has become increasingly competitive in the past few years, and product development is getting even more complex. The agility of an information system is a way to manage this complexity, and cloud technologies enable the sharing of tools and information in a new way. In particular, in the field of Product Lifecycle Management, a review of the literature demonstrates that there are technical issues that limit the efficient collaboration between the various stakeholders along the product lifecycle. Cloud manufacturing is a new concept that enables the virtualization of manufacturing resources and capabilities and provides them as a service. Therefore it offers new prospects for usage and collaboration in the extended enterprise, and along the product lifecycle. For instance, it helps to manage variations in production demand, by providing a large set of potential manufacturing resources on demand. However, to share the resources within the cloud manufacturing environment, a unification model of the domain information is required, to which any provider and/or user of cloud manufacturing must conform in order to dialog with the other CM stakeholders. This study provides an ontological model of the cloud manufacturing domain in order to support information exchange between the cloud manufacturing resources. The concepts of the proposed ontology are based on a literature review of models of cloud and models of manufacturing. The detailed ontology is then validated using the OntoClean methodology and within its implementation in an industrial scenario.

Cloud manufacturing resources fuzzy classification based on genetic simulated annealing algorithm

ABSTRACTTo solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.

Research on technologies and application of data mining for cloud manufacturing resource services

Nowadays, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for better implementing CMfg services is how to address the problems faced by distributed massive and polymorphic data analysis and processing of manufacturing resource services in CMfg environment. In order to handle the problems, and promote development integration and business application of data mining, the research on technologies and application of data mining for cloud manufacturing resource (CMR) services has been carried out in this paper. The data mining application model and multi hierarchy architecture of CMR services are designed, and the topology of resource service data integrating process based on multi-Agent is presented. Besides, the preprocessing method of manufacturing resource data is proposed, and a CMR virtual data warehouse is established as well. In addition, an improved genetic algorithm oriented to manufacturing resource services is put forward, so as to achieve efficient searching and mining of massive polymorphic data. Finally, a case study is employed to illustrate the effectiveness and applicability of the proposed method in CMR service platform.

The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system

Service composition and optimal selection (SCOS) technology composes basic services to satisfy various users’ needs and realize enterprise resource efficient allocation and value maximization. Since sharply increasing scale of the cloud manufacturing (CMfg) resource pool, and the growing sophistication of user requests, will make composed service a sharp increase in the quantity, type, dimension and complexity, which cause a big data environment for CMfg’s application and realization, this paper analyzes the difficulties and solutions of SCOS of big data in the future, especially for optimal selection from large-scale composed service execute paths (CSEP), and proposes two phases SCOS method based on case library. Firstly, cases, similar with users’ request, are searched from case library. Secondly, the cases are used to initial the existing optimization algorithm to solve large-scale optimal selection problem. Moreover, case library structure, user’s service request structure, similarity comparison, and realization process are studied. Compared to the existing optimization algorithm method, the prototype system using case library in large-scale CMfg could obtain a better optimization result.

Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set

In the manufacturing process on cloud system, there exist a lot of resource services which have similar functional characteristics according to functional requirements of a manufacturing task. How to select the optimal resources chain or resources combination to accomplish the certain manufacturing task with distributed subtasks is a key issue in the study of cloud manufacturing (CMfg). In this study, a multivariate process capability indicator is put forward to evaluate the manufacturing process with the reliability of data on the cloud decaying when time passing. According to the fuzzy quality theory, the dominance degree of intuitionistic fuzzy value is taken into account in decision making when selecting the optimal resources chain. A new method is proposed for resource service optimal selection based on multivariate process indicator and dominance degree of intuitionistic fuzzy value. A practice case study is used to illustrate the proposed method and procedure. The performance and advantage of the proposed method are discussed at the end.