scholarly journals IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Li-Nan Zhu ◽  
Peng-Hang Li ◽  
Xiao-Long Zhou
Keyword(s):  
New Product Development ◽  
Service Composition ◽  
Manufacturing System ◽  
Cloud Service ◽  
Optimization Method ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Parallel Hybrid ◽  
Manufacturing Service Composition ◽  
Cloud Service Composition

Cloud manufacturing (CMfg) is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on-demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

A Framework for Correlation Relationship Mining of Cloud Service in Cloud Manufacturing System

2011 ◽  
Vol 314-316 ◽  
pp. 2259-2262 ◽  
Author(s):  
Hua Guo ◽  
Lin Zhang ◽  
Fei Tao
Keyword(s):  
Manufacturing System ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Cloud Services ◽  
Added Value ◽  
Correlation Relationship ◽  
Function Modules ◽  
Key Issues ◽  
Manufacturing Resource ◽  
Cloud Service Composition

As a new manufacturing paradigm, cloud manufacturing (CMfg) is proposed to realize the added-value and on-demand use of manufacturing resource and ability in the form of manufacturing services. Considering that there always exist correlations among cloud services (CS), which affect the cloud service composition (CSC). Hence, how to mine the correlations among CSs and apply them to CSC is a key issue for realizing the added-value. This paper presents a framework for correlation relationship mining for CSC. Four function modules for mining correlations among CSs are analyzed, and the involving key issues were preliminarily discussed as well.

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

2021 ◽  
pp. 2150293
Author(s):  
V. Meena ◽  
N. Sasikaladevi ◽  
T. Suriya Praba ◽  
V. S. Shankar Sriram
Keyword(s):  
Service Composition ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Test Cases ◽  
Trust Value ◽  
Value Maximization ◽  
Teaching Learning Based Optimization ◽  
Cloud Service Composition ◽  
Teaching Learning

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 Teach Inglea Rning-based Optimization a Lgorithm (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.

Automatic Manufacturing Cloud Service Composition Based on AI Plan

2014 ◽  
Vol 513-517 ◽  
pp. 990-993 ◽  
Author(s):  
Pei Si Zhong ◽  
Shao Qi Zhu ◽  
De Jie Huang ◽  
Hai Liang Xin
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Cloud Service ◽  
Cloud Manufacturing ◽  
Search Method ◽  
Added Value ◽  
Service Environment ◽  
Manufacturing Resources ◽  
Cloud Service Composition ◽  
Automatic Manufacturing

Manufacturing cloud service composition is the key way to improve the utilization of manufacturing resources and manufacturing capabilities, realize added value and efficiency of manufacturing resources and manufacturing capabilities. It has great significance on cloud manufacturing implementation and carry. Therefore, the paper presents automatic forwarding search method called AMCSC-HFS for manufacturing cloud service composition based on AI plan. The main purpose is to support meeting the actual need of large-scale and dynamic cloud manufacturing cloud service environment.

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