Cloud Manufacturing Service Composition Research Articles

An efficient cloud manufacturing service composition approach using deep reinforcement learning

Cloud Manufacturing (CMfg) revolutionizes manufacturing by providing resources as cloud-based services. The main challenge in CMfg is identifying the best combination of these services to meet customer needs efficiently. This paper explores the potential of Deep Reinforcement Learning (DRL), a technology with successful applications in fields such as healthcare and transportation, to tackle scheduling challenges in CMfg. A tailored DRL environment for CMfg is introduced, along with a novel DRL-based algorithm designed to optimize service composition in CMfg. Comparative evaluations against existing algorithms, both in the new DRL-based CMfg environment and a benchmark DRL environment, consistently showed the superior performance of the proposed algorithm. Additionally, the study analyzes different combinations of weight coefficients and probabilities of service failure within the new CMfg environment, revealing their significant impact on scheduling outcomes. These findings underscore both the robustness and efficiency of the proposed solution, showcasing DRL’s potential to significantly enhance CMfg operations.

Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

A two-phase method to optimize service composition in cloud manufacturing

A two-phase method to optimize service composition in cloud manufacturing

An optimization method of cloud manufacturing service composition based on matching-collaboration degree

An optimization method of cloud manufacturing service composition based on matching-collaboration degree

A Hybrid MCDM Model for Service Composition in Cloud Manufacturing using O-TOPSIS

A Hybrid MCDM Model for Service Composition in Cloud Manufacturing using O-TOPSIS

Dynamic cloud manufacturing service composition with re-entrant services: an online policy perspective

Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold.

Trust Evaluation for Service Composition in Cloud Manufacturing Using GRU and Association Analysis

Service composition enables the flexible and agile collaboration of multiple services to complete personalized manufacturing tasks in cloud manufacturing. Compared with traditional manufacturing mode and cloud computing, trust problems become more serious and crucial in cloud manufacturing because of the nontransparency and short-term cooperation mode. A trust evaluation method for service composition in cloud manufacturing is proposed in this article. To quantitatively calculate the trust, a trust evaluation index system is established that comprehensively considers the influencing factors in the production, transaction, and collaboration processes of cloud manufacturing services. The trust value is synthesized based on all index values using the criteria importance through intercriteria correlation method. To extract the temporal information in historical trust data, a time-aware predictive trust evaluation method based on gated recurrent unit is proposed to learn the changing pattern of trust value over time. The trust data are trained together with their timestamps to predict the trust in the scheduled transaction time. The correlation between services in service composition is modeled to mine the correlation information by association analysis. The trust of service composition depends on the trust values of all component services and the correlations between them. The experiments demonstrate the effectiveness of the proposed method through case studies and performance comparisons with other state-of-art methods.

A Bi-Level Optimization Model for Service Composition in Cloud Manufacturing Based on Global Benefit and Correlation Effect

A Bi-Level Optimization Model for Service Composition in Cloud Manufacturing Based on Global Benefit and Correlation Effect

Collaborative optimization for energy saving and service composition in multi-granularity heavy-duty equipment cloud manufacturing environment

<p style='text-indent:20px;'>Efficient service scheduling is an important technique supporting collaborative manufacturing platforms such as cloud manufacturing. To achieve a more efficient task execution, heavy-duty equipment manufacturing, an important field of cloud manufacturing, must be explored beyond parameters of cost and time. The manufacturing service composition problem of heavy-duty equipment has the characteristics of task complexity, high process energy consumption, and multi-granularity nature of service (MGNoS). In the manufacturing process of heavy-duty equipment, the energy consumption of required logistics accounts for 30% the total energy consumption. However, to date, research has investigated the problem almost always from the task level, and MGNoS has received little attentions, which may lead to redundant energy consumption in logistics during manufacturing execution. In this paper, the problem of manufacturing service scheduling with integrating energy saving and service composition in cloud manufacturing is considered. Based on the mathematical description, a cross-granularity task chain reconfiguration algorithm is presented for mitigating the adverse effects of MGNoS and developing the adaptive non-dominated sorting genetic algorithm Ⅲ for solving the service composition scheme to generate optimal scheduling solutions. The effectiveness and efficiency performances of typical optimization algorithms are compared with the proposed approach. The results show that the proposed method achieves significant energy savings for all tasks in different scenarios.</p>

Dynamic Adaptive Reconfiguration Method for Cloud Manufacturing Service Composition in Practical Multi-constraint Environment

Dynamic Adaptive Reconfiguration Method for Cloud Manufacturing Service Composition in Practical Multi-constraint Environment

Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm - Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.

Towards Optimal Resources Allocation in Cloud Manufacturing: New Task Decomposition Strategy and Service Composition Model

Service composition optimization is one of the core issues in cloud manufacturing research. However, all current studies of service composition in cloud manufacturing assume that tasks have been decomposed into subtasks, so they can be directly mapped to existing services. However, due to the complexity, diversity, and multilevel of services in cloud manufacturing, services have different granularity. Therefore, the matching between tasks and services does not always occur at the lowest level. For solving the problem of discontinuity between task decomposition and service composition, this paper considers the characteristics of existing services in the cloud pool and proposes a task decomposition strategy based on task/service matching on the basis of refining the description model of tasks and services. Then, for the decomposed subtask set, the E-CARGO model is used to model the optimal composition process of services, and CPLEX is used to solve the model. Practical cases show that the proposed task decomposition strategy can solve the problem of discontinuity between task decomposition and service composition without relying on more expert systems. In addition, the proposed service composition model is more flexible, can easily model more variable factors, and CPLEX can solve the model more quickly and stably.

A three-tier programming model for service composition and optimal selection in cloud manufacturing

The process of service composition and optimal selection in cloud manufacturing (CMfg-SCOS) involves three types of users: service demanders, resource providers, and cloud platform operators. The interests of all users are a research focus of CMfg-SCOS, as their participation in the CMfg system directly affects the efficiency and long-term development of CMfg. However, the current research on CMfg-SCOS rarely considers the interests of all three types of users simultaneously, and the interest of resource providers is not clearly defined, which lags behind the reality of CMfg. Therefore, this study first proposes a three-tier programming model of CMfg-SCOS that considers the interests of service demanders, cloud platform operators, and resource providers. At the lower level of the model, service demanders are the decision makers, aiming to minimize time and cost and maximize service quality. At the middle level of the model, cloud platform operators are the decision makers, aiming to maximize resource use and flexibility in the face of uncertain environments. At the upper level, resource providers are the decision makers, aiming to maximize enterprise surplus. Then, this study develops an improved fast nondominated sorting genetic algorithm with advancement and inheritance (namely, a-i-NSGA-II) to solve the three-tier model efficiently. Numerical experiments conducted in this study found that in comparison to the art of state algorithms, including original nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and multiobjective spotted hyena optimizer (MOSHO), the proposed a-i-NSGA-II has better diversity and comprehensive performance at the middle level of the model and better solution quality at the upper level. Furthermore, a case study of the actual production of an automobile fuel tank assembly enterprise verifies the effectiveness of the proposed model and algorithm.

Cloud manufacturing service composition based on business matching optimisation

Cloud manufacturing service composition based on business matching optimisation

Cloud manufacturing service composition based on business matching optimisation

Cloud manufacturing service composition based on business matching optimisation

Adaptive multi-objective service composition reconfiguration approach considering dynamic practical constraints in cloud manufacturing

Dynamic uncertainty factors such as equipment faults are common in practically implemented cloud manufacturing (CMfg) environments, often causing the manufacturing service to be invalidated. In that case, efficient reconfiguration of the original service composition under practical constraints is critical; however, existing research scarcely focuses on it. This paper proposes a dynamic service composition reconfiguration model to bridge the gap by considering practical constraints (DSCRPC) in a real-life cloud manufacturing environment. Based on the constraints considered in this study, the DSCRPC model redefines three objectives: time (T*), cost (C*), and product service quality (Q*S*). To optimize the DSCRPC model, this study developed an adaptive multi-population multi-objective whale optimization algorithm (AMPOWOA) based on the Pareto strategy. The algorithm adopts four balancing strategies and adaptively optimizes and adjusts the key parameters under various balancing strategies through well-designed reinforcement learning models. Finally, we conduct numerical experiments and actual application case tests to compare the performances of AMPOWOA and other algorithms (MOWOA, MOHHO, NSGA-II). The results show that DSCRPC can continuously tackle the cloud manufacturing service composition (CMSC) reconfiguration issue with constraints until an order is completed. Moreover, AMPOWOA is superior to the other algorithms optimizing the DSCRPC model. This significantly enhances the robustness of service composition reconfiguration in real-life CMfg.

Trust Enforced Cloud Service Composition Based on Teaching–Learning-Based Optimization – STORM

In the arena of Cloud Computing, the emergence of social networks and IoT increased the number of available services on the cloud platform, making service composition and optimal selection (SCOS) in Cloud Manufacturing (CMfg), NP-hard. The existing approaches for addressing SCOS often fail to offer assistance with maximized trust and satisfied QoS preferences. Hence, this research paper presents a novel TeachIng leaRning-based Optimization aLgorithm (TIROL) for achieving the optimal solution for truST enforced clOud seRvice coMposition (STORM) to assist CMfg for improving the trust value with satisfied QoS preference(s). The performance of the proposed framework has been validated using the synthetic dataset generated from different test-cases. Experimental results show that the proposed framework is reliable and outperforms the SOTA approaches in terms of trust value maximization.

An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing

Composing existing services into one value-added composite service for a cooperative business process is especially attended by both academia and industry in the cloud manufacturing (CMF) environment from the perspective of industrial information integration. Various service composition methods based on the QoS have been proposed to satisfy the user's requirement. However, the traditional methods fail to consider interrelation among various services and change in QoS. The unstable QoS may affect the reliability of service composition, which may also affect reliable collaboration as cloud services need to cooperate with each other to accomplish the business process efficiently. In this paper, we propose an efficient two-phase approach by integrating clustering and Chaos-Gauss-based PSO to solve the above problem. In phase one, the K-means clustering algorithm is adopted to improve the quality of candidate services with the consideration of QoS stability for reducing the search space. In phase two, a novel multi-objective PSO algorithm based on Chaos-Gauss named CGPSO is proposed to find the optimal service composition. The Two-Phase approach considers both the QoS stability and service collaboration ability to reduce the probability of service composition failure. Further, we conduct a comprehensive analytical and experimental study to show that our approach has better performance and effectiveness for service composition than other approaches in the CMF environment.

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.