Bi-objective service composition and optimal selection for cloud manufacturing with QoS and robustness criteria-Reference-Cited by-同舟云学术

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Bi-objective service composition and optimal selection for cloud manufacturing with QoS and robustness criteria

Published:2022-10 Issue: Volume:128 Page:109530
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Gao Yifan,Yang Bo^ORCID,Wang Shilong,Zhang Zhengping,Tang Xiaoli

Publisher

Elsevier BV

Subject

Software

Reference49 articles.

1. Cloud manufacturing: A computing and service- oriented manufacturing model;Tao;Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.,2011

2. The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system;Xiang;Int. J. Adv. Manuf. Technol.,2016

3. Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system;Lin;J. Intell. Manuf.,2017

4. A classification-based approach for integrated service matching and composition in cloud manufacturing;Bouzary;Robot. Comput.-Integr. Manuf.,2020

5. Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments;Yaghoubi;Simul. Model. Pract. Theory,2020

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1. Priority-based two-phase method for hierarchical service composition allocation in cloud manufacturing;Computers & Industrial Engineering;2024-10

2. A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization;Journal of Global Optimization;2024-09-12

3. An adaptive robust service composition and optimal selection method for cloud manufacturing based on the enhanced multi-objective artificial hummingbird algorithm;Expert Systems with Applications;2024-06

4. Multi-trial Vector-based Whale Optimization Algorithm;Journal of Bionic Engineering;2024-04-26

5. Optimal selection of cloud manufacturing resources based on bacteria foraging optimization;International Journal of Computer Integrated Manufacturing;2023-06-26

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