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

Wang, Yankai; Wang, Shilong; Gao, Song; Guo, Xixuan; Yang, Bo

doi:10.1016/j.knosys.2021.107607

Cited by 31 publications

(4 citation statements)

References 31 publications

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“…Akbaripour, et al [16] also considered geographical location while estimating their QoS metric for time, cost, and quality in service composition. Wang, et al [17] considered customer satisfaction and service quality while, Wang, et al [18] considered the uncertainty factor of faulty equipment to improve the QoS in CMfg. Therefore, these differentiated QoS can provide leverage to RSP by adjusting prices independently.…”

Section: Literature Reviewmentioning

confidence: 99%

Differential pricing integrated with multi-product, multi-machine, multi-worker cost function for resource service providers in cloud manufacturing

Qamar

Aziz

2023

Mehran Univ. res. j. eng. technol.

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Cloud manufacturing (CMfg) platform offers an open marketplace, where a Resource Service Provider (RSP) can benefit from Price Discrimination (PD) in return for specific services. However, the literature focused mainly on operator-based pricing and overlooked RSP pricing control. Therefore, this study formulates a profit function in which revenue is enhanced by adjusting prices according to customer types, while cost accounting is done by resource allocation based Material Flow Cost Accounting (MFCA) because MFCA provides a comprehensive guideline towards waste minimization. The proposed model is formulated into MINLP problem with multiple factors such as; part types, batch size, part routes, machine types, energy consumption, worker types and material handling cost as well as price sensitive customer behaviour and demand. Further, ANOVA is applied for factors analysis. The results suggests that customer types and demand are positively correlated, while parts, machines, and worker types are negatively correlated with profit. The model is also compared with reference price effect and fixed pricing strategy. Results validate that to benefit from diverse customer behaviour in CMfg, PD along with optimal resource allocation provides an effective solution for profit maximization. Model is also compared with reference price effect and fixed pricing strategy to validate its effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Differential pricing integrated with multi-product, multi-machine, multi-worker cost function for resource service providers in cloud manufacturing

Qamar

Aziz

2023

Mehran Univ. res. j. eng. technol.

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“…A set of Pareto-optimal solutions are provided to satisfy varied preferences. Wang et al [11] proposed an improved whale optimization algorithm to divide the population into several populations. A pareto strategy is presented to improve the optimization.…”

Section: Pareto-based Multi-objective Service Compositionmentioning

confidence: 99%

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Peng

Guo

2023

Computational Intelligence and Neuroscience

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In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine-grained search. The proposed approach has been tested on a real-world dataset that locates a representative set of solutions with proximity and distributivity.

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“…Dynamic service composition problem has been studied extensively. Dynamic service composition methods consider the volatility of QoS attributes of Web services, and dynamically adjust the service composition scheme to meet the global QoS constraints of users during the operation of composite services [9]. Dynamic service composition methods can be divided into strategy-based approaches, replacement-based approaches and reinforcement learningbased approaches.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning

Yuan,

Guo,

2023

IEEE Access

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Automatically generating service composition solutions that meet user application requirements is one of the hot research topics in the field of service composition in the context of Web service big data. To address the challenges of accurately obtaining reward function values and significant increase in time complexity when dealing with large-scale data in the context of reinforcement learning-based service composition, this paper proposes a novel approach that combines zero-sum game and inverse reinforcement learning. The proposed method models the service composition problem as a Markov Decision Process (MDP) and dynamically adjusts the service composition solution by solving for the optimal policy. By leveraging runtime records of service composition operations, we generate an expert experience dataset and develop a novel inverse reinforcement learning algorithm based on the integration of zero-sum game principles. Experimental results demonstrate that the proposed method effectively reduces the dependence of the inverse reinforcement learning algorithm on the quality of expert experience data and reduces the time cost of service composition.INDEX TERMS Service composition, inverse reinforcement learning, zero-sum game, quality of service.

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

Cited by 31 publications

References 31 publications

Differential pricing integrated with multi-product, multi-machine, multi-worker cost function for resource service providers in cloud manufacturing

Differential pricing integrated with multi-product, multi-machine, multi-worker cost function for resource service providers in cloud manufacturing

Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm

Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning

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