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

Lim, Ming K.; Xiong, Weiqing; Wang, Yankai

doi:10.1016/j.cie.2022.108006

Cited by 30 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The whole process of cloud manufacturing service portfolio optimization mainly involves three subjects: service demander, platform operator and service provider [26]. Wang et al constructed an evolutionary game model of ''service demander-service provider-platform operator,'' and studied the trust relationship among the three subjects in the cloud manufacturing environment [27].…”

Section: B Construction Of Cloud Manufacturing Service Composition Op...mentioning

confidence: 99%

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Guo

Niu

2023

IEEE Access

View full text Add to dashboard Cite

In order to solve the problems such as the dynamic change of historical attribute service evaluation indicators, the lack of comprehensive consideration of the interest needs of all cloud manufacturing participants, and the strong subjectivity of the composition optimization results in the process of cloud manufacturing service composition. Taking the demands of service demanders, platform operators and service providers as constraints, this paper constructs a multi-objective optimization model of cloud manufacturing service composition that comprehensively considers multi-agent interests, and introduces the time decay function to deal with the service evaluation indicators with historical attributes, which reduces the impact of the dynamic changes of service evaluation indicators. Secondly, this paper adopts the NSGA-II with the elite selection strategy to solve the cloud manufacturing service composition optimization (SCO) model, and uses the grey target decision-making method to select the optimal solution from the Pareto solutions obtained by the NSGA-II, which avoids the problem of strong subjectivity in service composition decision-making. Finally, through case analysis, it was found that the bull's-eye distance of the optimal solution obtained by the NSGA-II was reduced by at least 34.95% compared to the genetic algorithm, verifying the feasibility of the optimization model and the effectiveness of the algorithm.

show abstract

Section: B Construction Of Cloud Manufacturing Service Composition Op...mentioning

confidence: 99%

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Guo

Niu

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Constraints (2) Ensure that each CMfg task can only be assigned to one available CMfg service provider. Constraint (3) is a subtask prioritization constraint, that is, a subtask belonging to the same CMfg task must start after the subtask that precedes it completes.…”

Section: J Imentioning

confidence: 99%

“…CMfg is an advanced networked intelligent manufacturing model, which includes a large number of new product customization services [1]. In the past few years, the research on the optimization of CMfg services has mainly focused on service combination and the scheduling of CMfg tasks, Lim et al [2] studied the service combination and optimization selection problem considering the interests of service demanders, cloud platform operators and resource providers. Laili [3] studies the multi-stage integrated scheduling of hybrid tasks in the CMfg environment.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning-Variable Neighborhood Search Method for the Cloud Manufacturing Scheduling Robust Optimization Problem with Uncertain Service Time

Wang,

2023

Advances in Economics, Business and Management Research

View full text Add to dashboard Cite

Cloud manufacturing (CMfg) is an advanced networked intelligent manufacturing model, which includes a large number of new product customization services. Since many products lack historical data on service time, there is uncertainty about CMfg product service time, thus, CMfg service platforms need to perform robust scheduling of CMfg services for new products. In this paper, a CMfg scheduling model considering service time uncertainty and nonpredefined service paths is constructed, and its robust equivalent is derived. In order to effectively solve the above model, this paper proposes a reinforcement learning-variable neighborhood search algorithm (rVNS) based on the variable neighborhood search algorithm, in which the upper confidence bound algorithm (UCB1) is used to adaptively select the neighborhood operator. To solve the problem of insufficient historical data at its cold start, the SARSA (lambda) method is used in this paper. In addition, this paper leverages adaptive windows to estimate and detect changes in rewards in data streams to obtain more accurate reward estimates. A large number of experiments prove that the algorithm designed in this paper has high accuracy and speed advantages in solving this problem.

show abstract

“…It combines atomic services in different areas to meet the needs of users with multiple functions and different QoSs. The service composition approach has been widely used in different industries, such as cloud manufacturing [7,9,10] and metro services [11].…”

Section: Introductionmentioning

confidence: 99%

“…Most service composition methods take into account the user's QoS. These QoSs include both hard requirements [12][13][14][15], such as time limits [3,[16][17][18] and costs [6,[19][20][21][22], and soft requirements [11,18,[23][24][25][26][27][28][29][30][31][32], such as security [4,5,9,10,33], reliability and flexibility.…”

Section: Introductionmentioning

confidence: 99%

Service composition considering energy consumption of users and transferring files in a multicloud environment

Zhu

2023

J Cloud Comp

View full text Add to dashboard Cite

In the internet and cloud environment, service composition is always used to enhance the function and processing ability of clouds. Those clouds work together for a user and provide different functions. A service request may involve multiple clouds. The past work focuses on the method of service composition and ignores the energy composition when files are transferred between clouds, including the energy consumption for transferring files (sending files from the user to the cloud and receiving files from the cloud to the user) of the user. The paper models the service composition in a multicloud environment. Based on those models, we use the GA (genetic algorithm) algorithm (GA-C) to solve the service composition problem with multiple targets in a multicloud environment. Simulation results show that the GA-C can: (1) reduce the average number of involved clouds and the energy consumption between clouds, and (2) reduce the energy consumption of the user and the failure rate of service composition.

show abstract

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

Cited by 30 publications

References 48 publications

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

Multi-Agent Interests Service Composition Optimization in Cloud Manufacturing Environment

A Reinforcement Learning-Variable Neighborhood Search Method for the Cloud Manufacturing Scheduling Robust Optimization Problem with Uncertain Service Time

Service composition considering energy consumption of users and transferring files in a multicloud environment

Contact Info

Product

Resources

About