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.

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.

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.

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.

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.

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, 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.

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.