To tackle challenges of low profitability and high response delays in multitask competitive production environments with fluctuating capacity, this article studies a collaborative optimization approach of task admission and service scheduling with dynamic pricing aid. First, in the context of platform-aggregated manufacturing service collaboration, we introduce service queues to account for response delays and develop a novel nonlinear profit optimization model. This model optimizes task admission, service scheduling, and pricing decisions simultaneously, to adapt the admitted task load to the fluctuating capacity and enhance the throughput utilities of heterogeneous services. To solve this large-scale, nonlinear optimization problem, we then propose a novel distributed online task admission and service scheduling optimization strategy by constructing a Lyapunov quadratic function. It coordinates the optimal decisions for each service in a computationally efficient manner without requiring prior knowledge of task statistics or data training. Moreover, we analytically illustrate that our approach can achieve the optimal time average profit while bounding time average queue length over temporal fluctuations. Numerical results from real workload traces demonstrate the effectiveness of our approach compared to three existing strategies, offering valuable insights for platform operations.

Traditional knowledge-based situation awareness (SA) modes struggle to adapt to the escalating complexity of today’s Energy Internet of Things (EIoT), necessitating a pivotal paradigm shift. In response, this work introduces a pioneering data-driven SA framework, termed digital twin-based SA (DT-SA), aiming to bridge existing gaps between data and demands, and further to enhance SA capabilities within the complex EIoT landscape. First, we redefine the concept of digital twin (DT) within the EIoT context, aligning it with data-intensive scientific discovery paradigm (the Fourth Paradigm) so as to waken EIoT’s sleeping data; this contextual redefinition lays the cornerstone of our DT-SA framework for EIoT. Then, the framework is comprehensively explored through its four fundamental steps: digitalization, simulation, informatization, and intellectualization. These steps initiate a virtual ecosystem conducive to a continuously self-adaptive, self-learning, and self-evolving big model (BM), further contributing to the evolution and effectiveness of DT-SA in engineering. Our framework is characterized by the incorporation of system theory and Fourth Paradigm as guiding ideologies, DT as data engine, and BM as intelligence engine. This unique combination forms the backbone of our approach. This work extends beyond engineering, stepping into the domain of data science—DT-SA not only enhances management practices for EIoT users/operators, but also propels advancements in pattern analysis and machine intelligence (PAMI) within the intricate fabric of a complex system. Numerous real-world cases validate our DT-SA framework.

Rapid growth of diversity, uncertainty, and coupling effect of units in modern energy systems jointly challenges the traditional model-based situation awareness (SA) in Energy Internet of Things (EIoT). This work explores the digital twin of EIoT (EIoT-DT) and then provides a novel data-driven SA paradigm, named DT-SA, as a promising alternative. Based on the combination of the latest data technologies and machine learning algorithms, DT-SA transfers those stubborn SA challenges to digital space, and then addresses them by building a domain-specific and data-friendly digital twin (DT) model upon massive data. The established model can be quantitatively tested via iterative virtual–real interaction and, thus, be evaluated and updated through closed-loop feedback to improve its performance in the physical world. To this end, some engineering and scientific problems are raised: 1) virtual–real interaction mechanism relevant to resource flow and data flow; 2) unified modeling and analysis of heterogeneous spatial–temporal data; 3) DT configuration and evolution; and 4) domain-specific DT-SA characterization. To solve these problems, cloud-edge-terminal configuration, big data analytics (BDA), DT, and SA indicator systems are studied, respectively. Then, the random matrix theory (RMT) and overarching DT-SA framework are designed as a roadmap. Besides, some potential applications and undergoing projects on the terminal, edge, or cloud are discussed, e.g., condition assessment of equipment, digital monitoring and diagnosis of the power grid network, and EIoT construction in the smart city. Finally, some perspectives and recommendations are proposed in conclusion for future research. This research can be regarded as an efficient handbook for both energy engineering and data science, which may benefit enterprise digitization, smart city, etc.

Manufacturing service collaboration (MSC) provides a low cost, high efficiency, and good quality collaboration diagram on Industrial Internet Platforms. However, some stakeholders areunwilling to participate in MSC continuously due to their short-term dissatisfaction outbursts or long-term dissatisfaction accumulation with MSC. To describe the aforementioned status of stakeholders, collaboration tiredness is first defined and its causes and impacts on MSC are analyzed. To avoid massive loss of stakeholders on the platform, it is urgent to study stimulus methods to improve their satisfaction. In this article, long-term dynamic utility models are established to depict changes in stakeholder satisfaction, including short-term utility models and long-term utility updating criteria. Then, an incentive strategy is proposed, which aims at stimulating the collaboration willingness of consumers with collaboration tiredness. Once consumers with collaboration tiredness are detected through Bayesian thresholds, utility references would be generated. Then, to reach the utility references, MSC optimization would be continuously provoked to generate new plans through the improved memetic algorithm. Finally, experiments verify the effectiveness of the proposed incentive strategy from a long-term perspective.

With the development of new-generated IT technologies and the launch of a series of industrial Internet of things platforms, service-oriented manufacturing is an inevitable trend of manufacturing industry. Therefore, the platform-based manufacturing service collaboration (MSC) becomes a recognized answer to the complex and personalized manufacturing demands. However, the changes in both supply and demand of the platform in its operation process are usually unpredictable. To cope with the scheduling problem on the platform-based MSC with the dynamic uncertainties of both supply and demand, an adaptive scheduling mechanism is explored in this article. In which, the real-time system state evaluation method considering supply and demand are designed, and a supply-demand aware rescheduling trigger judgement is proposed. Experimental results show the effectiveness and adaptiveness of the proposed mechanism, which also provides a reference for other MSC scheduling problems towards different dynamic situations.

End-face defect control (EF-DC) of the hot-rolled coil is crucial to the quality management. The core of EF-DC is to identify defects accurately, predict defects in advance, and improve production in time to prevent similar defects. The current research works on EF-DC are mainly at the defect recognition stage, which merely rely on the operating data in physical space. However, different types of defects exist in coils with different reasons, therefore it is difficult to accurately control defects in time only through the hot rolling operating data. In order to solve the above problems, first an EF-DC framework based on digital twin with cloud-edge collaboration is designed in this article. The computing tasks are reasonably allocated through cloud-edge collaboration to realize the timeliness of control. Second, the virtual model of hot-rolled coil is constructed from four aspects: geometry, physics, behavior, and rules. Through the analysis of the defect mechanism, the abnormal behavior events are obtained, and the mapping relationship between the defect and the behavior event is established to realize the defect traceability and control. Finally, the feasibility of the proposed method is verified by taking the edge scratch defect as an example.

The Industrial Internet platform-based manufacturing service collaboration has made it possible for decentralized manufacturing enterprises to cooperate on a broad scale. The selection conflict problem may arise when many providers choose the same task at the same time, especially when there are more providers than consumers. At this moment, how to choose the appropriate manufacturing services, which should both satisfy the users’ requirements and enhance the participation of the providers, is of utmost importance. To increase the number of collaboration chances, the functional and quantitative manufacturing service collaboration is carried out simultaneously in this article. The variable utility models are used to represent the satisfaction levels of users while taking into account the bilateral coupling between providers and consumers and the unilateral irrationality of the provider. Finally, it is advised to choose providers with a short-term preference in situations where there is a strict time limit based on the findings and analysis of the manufacturing service collaboration optimization. At the same time, we can find that the providers, who get lower utilities when they focus on the current, are more likely to get greater utilities than others when they concentrate on long-term gains.

The automated conveyor system, as the core component in the modern manufacturing world, has gained lots of attention from researchers. To optimize the operation of the conveyor system, range-inspection control (RIC) has been considered an efficient strategy to bring this conventional technology to an intelligent level. Various algorithms have been put into use to achieve optimal control. However, the current methodologies are only focusing on control optimization, not scaled into the smart manufacturing framework. The schema of alignment and corporation between the physical and virtual spaces for the system remains an important problem. Therefore, the work in this article aims for an effective framework of implementation between the physical and virtual stations in an automated conveyor system. Since increasingly more application scenarios rely on the digital twin (DT) technology to realize the integration of physical and virtual systems, we proposed the DT automated conveyor system (DT-ACS) that constructs the road map to implement the RIC-based conveyor system under the background of a smart factory. Besides, profit-sharing-based deep Q-networks (PDQNs) have been proposed to cope with the RIC optimization problem. The robustness and efficiency of the proposed PDQN were evaluated via sets of experiments. The discussion and conclusion are presented at last accordingly.Note to Practitioners—This article aims to propose a strategy of control optimization for conveyor-based manufacturing systems under the digital twin (DT) framework. The conveyor system can be flexible to control the running flows to avoid overloading workstations. Due to the complex environment in the production line, the range that is able to be inspected and the capacity of the reserve area can be considerably diverse among the workstations. To maximally evaluate our framework, we set a comparatively complex environment for the experiments. Nevertheless, to obtain practically ideal performance under other circumstances, the parameters should be precisely tested and fine-tuned with simulation in advance.

Manufacturing service (MS) collaboration promotes the social collaboration of distributed enterprises, which makes profits through manufacturing resource sharing on platforms. Therefore, the pricing strategy for MSs will affect the collaboration results, and the satisfaction of enterprises with the platform further. Hence, in this article, a personalized dynamic pricing based MS collaboration optimization method is proposed. First, due to the poor information of the enterprise preference, long-term and short-term preferences of enterprises are estimated based on scarcity, including service features, service quantity, and available time. Then, the personalized pricing method is proposed to adapt to the dynamic collaboration process. And the consumer utility model that considers time decay and price changes is constructed, which reflects the utility characteristics of consumers in the actual collaboration process, such as the utility decrease and deceleration rate increase with time consuming. Finally, Q-learning algorithm based MS collaboration optimization verifies the effectiveness and superiority of the method.

As a series of industrial Internet platforms have been launched, manufacturing facilities in the physical world, although distributed in different enterprises, are interconnected in the form of manufacturing services (MSs) in cyberspace. In this context, it makes possible for on-demand sharing of MSs as well as corresponding cross-enterprise collaboration. However, many dynamic uncertainties of both MSs and the submitted demands occur unpredictably, which seriously hinders the platforms' applications. To cope with the problem of MSs scheduling with supply-demand dual dynamic uncertainties, a three-stage approach based on an evolutionary hypernetwork model is proposed. In which, six of nine kinds of dynamic events and eighteen specific conditions are considered, and an event-condition-act mechanism is designed to guide local/global rescheduling if needed. Experimental results show the effectiveness of the proposed approach, as well as the potential of a platform employing the approach in response to different dynamic events in its application.

Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set.

Virtualization is a crucial technology of cloud computing to enable the flexible use of a significant amount of distributed computing services on a pay-as-you-go basis. As the service demand continuingly increases to a global scale, efficient virtual machine consolidation becomes more and more imperative. Existing heuristic algorithms targeted mostly at minimizing either the rate of service level agreement violations or the energy consumption of the cloud. However, the communication overhead among different virtual machines and the decision time of virtual machine consolidation are rarely considered. To reduce both the over-utilized nodes and the under-utilized nodes with the consideration of migration cost, communication overhead, and energy consumption, this paper presents a new iterative budget algorithm in which a budget heuristic and a multi-stage selection strategy are designed to find suitable migration objects and targets simultaneously. Experiments show that the proposed algorithm provides a substantial improvement over other typical heuristics and metaheuristic algorithms in reducing the energy consumption, the number of migrated virtual machines, the overall communication overhead, as well as the decision time.

With the deeper application of sensor & cloud-based environment into manufacturing, deploying the industrial Internet platforms toward smart manufacturing has been more concerned. Based on the platforms, ubiquitous enterprises could participate in and support cross-enterprise collaboration, so that their distributed manufacturing facilities and capabilities could be shared and utilized in the form of manufacturing services (MSs). However, in order to achieve the successful application of the platforms, how to settle the supply demand matching (SDM) of the distributed manufacturing facilities and capabilities in the form of MSs, namely, MSs-SDM, becomes one of the most urgent problems to be solved. In addition, the trend of manufacturing socialization makes this problem much more scalable. In this context, this article aims to establish a set of hypernetwork-based models for the scalable MSs-SDM problem at first. An enterprises collaborative network is derived which is the projection of the underlying MSs-SDM situation to the upper-layer enterprises. Second, a method according to the evaluation on the cross-enterprise collaboration is proposed for this problem. In which, the created utilities, the rates of service invocation, and task allocation from both the global view of the overall network and the local view of each participated enterprise are evaluated. Finally, two steps of experiments introducing scalabilities illustrate the feasibility of the proposed models and the effectiveness of the derived method for MSs-SDM optimization, and further reveal five managerial implications to improve the operation and industrial practice of the platforms.

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

Integrated and collaborative manufacturing system develops as massive data are obtained by the Internet of Things (IoT) technology. However, the “trust tax” imposed on manufacturers during their countless collaborations with customers, suppliers, distributors, governments, service providers, and other manufacturers is very high. Blockchain is an emerging technology that can lead to more transparent, secure, and efficient transactions. It represents a new paradigm, as well as new thinking, of how data can be securely stored, integrated, and communicated among different stakeholders, organizations, and systems that unnecessarily trust each other. Blockchain is greatly useful for reducing the “trust tax,” especially beneficial for the small- and medium-sized enterprises that must tolerate much heavier trust tax than the established manufacturers. This paper investigates the blockchain-based security and trust mechanism and elaborates a particular application of blockchain for quality assurance, which is one of the strategic priorities of smart manufacturing. Data generated in a smart manufacturing process can be leveraged to retrieve material provenance, facilitate equipment management, increase transaction efficiency, and create a flexible pricing mechanism. The dairy industry is used to instantiate the value propositions of blockchain for quality assurance.

Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

As numerous Industrial Internet platforms emerge, manufacturing services are shared among multiple stakeholders more frequently than ever before. The optimal selection of shared manufacturing service composition (MSC) should promise both the task completion and the stakeholders' satisfaction. However, as commercial entities, stakeholders concentrate on not only the temporary benefits but also the long-term acquisitions. Most of the existing MSC problems neglect the stakeholders' prospect on the manufacturing service sharing. This leads to the disappointment and dissatisfaction of the stakeholders with long-term expectations, who will abandon the participation in Industrial Internet platforms. Therefore, the long/short-term preferences of various stakeholders should be satisfied and balanced. In this paper, the long/short-term utilities of three parties (provider, consumer, and operator) are first defined and discussed, and the models considering short-term utility of a consumer and long-term utility of providers are established. The potential tasks assigned to providers are taken into account to estimate the long-term utility if the current task is accepted. Then, to solve the biobjective optimization problem, an improved Nondominated Sorting Genetic Algorithm-II algorithm, combining Tabu search and improved K-means mechanism, is proposed to find the optimal solution set. Finally, the effectiveness of the method is verified by the experimental results in terms of solution diversity, astringency, and stability, in which a finding is further observed that the changes of consumers' preferences have little impact on the long-term utility of providers.

Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework-New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.

An increasing number of cities are implementing bicycle sharing systems to reduce traffic congestion. Determining the locations of bicycle stations is one of the fundamental challenges in planning of such systems. This paper provides a novel solution on it. The min-plus algebra is introduced to model transport systems for accessibility analysis. A unified model in the sense of the min-plus algebra for an integrated system with both buses and bicycles is presented to dynamically describe the state transitions of passengers in the system. A new accessibility is proposed with regards to a general index ω such as the geographical distance and the travel time. A necessary and sufficient condition on the accessibility is then provided. The minimization of bicycle stations under the accessibility is formulated to be a 0-1 integer programming problem. The case studies on two cities, Ningbo and Hangzhou, were performed, which show that compared with the current layouts of urban transportation networks, the proposed public bicycle sharing systems have remarkable advantages in topological characteristics and robustness against failures.

Smart manufacturing is increasingly becoming the common goal of various national strategies. Smart interconnection is one of the most important issues for implementing smart manufacturing. However, current solutions are not tended to realize smart interconnection in dealing with heterogeneous equipment, quick configuration and implementation, and online service generation. To solve the issues, industrial Internet-of-Things hub (IIHub) is proposed, which consists of customized access module (CA-Module), access hub (A-Hub), and local service pool (LSP). A set of flexible CA-Modules can be configured or programed to connect heterogeneous physical manufacturing resources. Besides, the IIHub supports manufacturing services online generation based on the service encapsulation templates and also supports quick configuration and implementation for smart interconnection. Furthermore, related smart analysis and precise management have the potential to be achieved. Finally, a prototype is given to illustrate the functions of the proposed IIHub, and to show how IIHub realizes smart interconnection.

With the increase of dimensions and complexity of current engineering problems, parallel evolutionary algorithm which take advantage of population division and information exchange among processors has been introduced for years. However, low solution ability of each sub-group and high communication load between them are always seen as the biggest bottlenecks which hinder parallel evolutionary algorithm to be more efficient. To overcome this two problems, a multi operators-based partial connected parallel evolutionary algorithm, i.e. MO-PCPEA is proposed. By combining multiple evolutionary operators, an adaptive strategy for operator configuration inside each parallel group is designed to ensure the searching ability of the algorithm for wider range of problems. More importantly, a partial connection topology is proposed to guide the periodic communication between each group. Computational results in two typical permutation combinatorial optimization benchmarks and one practical case study demonstrate that MO-PCPEA is highly competitive compared with most tailored serial and parallel evolutionary algorithms in terms of not only searching time, but also solution quality.

Cloud computing is getting more prevalent and finding a way to reduce the cost of cloud computing platform through the migration of virtual machines (VM) is a concerned issue. In this paper, the problem of dynamic migration of VMs (DM-VM) in the cloud computing platform (or simply the cloud) is investigated. A triple-objective optimization model for DM-VM is established, which takes energy consumption, communication between VMs, and migration cost into account under the situation that the platform works normally. The DM-VM problem is divided into two parts: (i) forming VMs into groups, and (ii) determining the best way to place the groups into certain physical nodes. A binary graph matching-based bucket-code learning algorithm (BGM-BLA) is designed for solving the DM-VM problem. In BGM-BLA, bucket-coding and learning is employed for finding the optimal solutions, and binary graph matching is used for evaluating the candidate solutions. The computational results demonstrate that the proposed BGM-BLA algorithm performs relatively well in terms of the Pareto sets obtained and computational time in comparison with two optimization algorithms, i.e., Non-dominated Sorting Genetic Algorithm (NSGA-II) and binary graph matching-based common-coding algorithm.

Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.

Energy-saving and emission-reduction (ESER), carbon footprint, carbon labeling, and carbon trading have attracted much attention recently due to severe environmental challenges. One key technology for implementing the above concepts is how to realize the effective quantitative evaluation of ESER. In this paper, the existing ESER evaluation technology and systems are summarized first. It is found that the existing ESER evaluation technology and systems are almost isolated from the existing enterprise information systems, such as enterprise resource planning (ERP), product data management (PDM), and customer relationship management (CRM), which results in the expanding of the enterprise information islands. In order to address this problem, a new method for ESER life cycle assessment (LCA) based on Internet of Things (IoT) and bill of material (BOM) is proposed in this paper. A four-layered structure (i.e., perception access layer, data layer, service layer, and application layer) ESER LCA system based on IoT and BOM is designed and presented, as well as the key technologies and the functions in each layer. A prototype application system is developed to validate the proposed method. The main contributions of the proposed method are: 1) facilitate real-time intelligent perception, and the collection of energy consumption and environmental impact data generated in the entire life cycle of manufacturing by using IoT technologies; and 2) realize effective data integration between the ESER evaluation system and the existing enterprise information systems based on BOM.

Recently, 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 implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.

In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.

The new networked manufacturing mode, cloud manufacturing (CMfg), is provided with a new operation and transaction mode. To support the research, development and application of the mode and service platform of CMfg, manufacturing resource and capability cloud service transaction (CST) of the tripartite users (i.e., provider, operator and consumer) is described briefly, and the detailed transaction flow is provided. With the characteristics of different cloud services (CSs), considering the multi-layer of logistics, information flow and capital flow, the transactions on hardware-class, software-class, product-class and capability-class CSs are analyzed respectively. Finally, the important and difficult problems urgently to be solved in the whole CST process are pointed out.

Energy saving and emission reduction (ESER) has caught attention around the world due to the severe environmental challenges. An Evaluation System to assess the consumption and emission of a product during its life cycle is proposed in this paper. The system integrates with the enterprise information systems by adding the consumption and emission data into BOM(bill of material). Based on ESER database, the whole system takes product life cycle as the main line and improves the accuracy of evaluation on ESER. Besides, design and optimization of green manufacturing can also be accomplished by using this system. The protype and framework of designed ESER system is described in this paper.

In distributed manufacturing systems, especially in a manufacturing grid (MGrid) system, there are primarily two kinds of manufacturing tasks (or resource service requests): (1) single resource service request task (SRSRTask), which can be completed by invoking only one resource service, and (2) multi-resource service request task (MRSRTask), which is completed by invoking several resource services in a certain sequence. For an SRSRTask, the system searches the resource services that are qualified for its function requirements and chooses the optimal one to execute it. For an MRSRTask, in addition to the search for all qualified resource services according to each subtask, the system selects one candidate resource service for each subtask. Then the system generates a new composite resource service (CRS) and selects the optimal resource service composite path from all possible paths to execute the task with the given multi-objective (e.g., time minimization, cost minimization, and reliability maximization) and constraints. The above problem is defined as multi-objective MGrid resource service composition and optimal-selection (MO-MRSCOS) problem in this paper. The formulation is presented for an MO-MRSCOS problem to minimize execution time and cost, and maximize the reliability. The basic resource service composite modes (RSCM) for CRS are described, and the principles for translating a complicated RSCM into a simple sequence RSCM are presented for simplifying the resolving process and complexity of MO-MRSCOS. A new MGrid resource service composition and optimal-selection method, based on the principles of particle swarm optimization (PSO), is then proposed. The PSO follows a collaborative population-based search, which models based on the social behavior of bird flocking and fish schooling. The case study demonstrates that the proposed method is useful in solving MO-MRSCOS problems. The experimental results and performance comparison show that the proposed method is both effective and efficient.