Multi-task oriented service composition in cloud manufacturing

• Article •

Multi-task oriented service composition in cloud manufacturing

LIU Wei-ning1,2，LIU Bo1,2+，SUN Di-hua2,3

1.School of Computer Science, Chongqing University, Chongqing 400030, China；2.Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education,Chongqing University, Chongqing 400030, China；3.School of Automation, Chongqing University, Chongqing 400030, China

Received:2013-01-25 Revised:2013-01-25 Online:2013-01-25 Published:2013-01-25

面向多任务的制造云服务组合

刘卫宁1,2,刘波1,2+,孙棣华2,3

1.重庆大学计算机学院，重庆400030；2.重庆大学信息物理社会可信服务计算教育部重点实验室，重庆400030；3.重庆大学自动化学院，重庆400030

Abstract

Abstract: To cope with the Multi-task Oriented Manufacturing Cloud Service Composition (MO-MCSC) problem in cloud manufacturing system, a mathematical model and a solution algorithm were proposed and studied. Based on single-task oriented manufacturing service composition, the Quality of Service (QoS)-aware MO-MCSC model was formulated by integrating corresponding assumptions and principles of multi-task multi-coalition generation problem. A matrix real-coded based genetic algorithm was proposed based on the analysis and properties of MO-MCSC model. In this algorithm, the row-based crossover/mutation operator, column-based crossover/mutation operator and point-based crossover/mutation operator were designed to adapt matrix coding manner. Meanwhile, legality repairing strategy of individuals was designed by considering the related constraints of problem model. Simulation experiment results showed that the proposed algorithm could solve MO-MCSC problem efficiently and effectively.

Key words: multi-task, cloud manufacturing, service composition, genetic algorithms

摘要： 针对云制造系统中面向多任务的制造云服务组合问题，研究建立了问题模型并提出了求解算法。以面向单任务的制造服务组合方法为基础，融入多任务多联盟生成问题的相关假设和基本原则，建立了基于服务质量的多任务云服务组合模型。继而基于对该模型特征的分析与把握，提出基于矩阵实数编码的改进遗传算法。该算法设计了与矩阵实数编码相适应的行交叉/变异、列交叉/变异和点交叉/变异算子；同时基于对问题模型相关约束的考虑，设计了合法性修复策略。仿真实验表明，该算法能有效并以较高效率求解面向多任务的制造云服务组合问题。

关键词: 多任务, 云制造, 服务组合, 遗传算法, 矩阵实数编码

CLC Number:

TH166

LIU Wei-ning1,2，LIU Bo1,2+，SUN Di-hua2,3. Multi-task oriented service composition in cloud manufacturing[J]. .

刘卫宁1,2,刘波1,2+,孙棣华2,3. 面向多任务的制造云服务组合[J]. .

[1]	. Clinical pathway optimization based on variations [J]. , 2021, 27(9): 2556-2564.
[2]	. Service dependency identification based on service network [J]. , 2021, 27(9): 2691-2700.
[3]	. Task scheduling method for large-scale factory access in cloud and edge collaborative computing architecture [J]. , 2021, 27(8): 2282-2294.
[4]	. Lot-splitting and scheduling algorithm of multi-level assembly job shops [J]. , 2021, 27(8): 2307-2320.
[5]	. Multi-timing production of differentiated products in cloud manufacturing mode [J]. , 2021, 27(6): 1681-1692.
[6]	. Cloud manufacturing service composition optimization based on reliability and credibility analysis [J]. , 2021, 27(6): 1780-1798.
[7]	. Tracking and registration method based on image matching for augmented reality aided assembly system [J]. , 2021, 27(5): 1281-1291.
[8]	. Evaluation of cloud manufacturing service quality evaluation based on intuitionistic fuzzy cosine similarity [J]. , 2021, 27(4): 1128-1134.
[9]	. Modeling and optimization of corridor allocation problem considering irregular logistics interaction points [J]. , 2021, 27(4): 1155-1166.
[10]	. New hybrid improved genetic algorithm for solving no-wait flow shop scheduling problem [J]. , 2021, 27(3): 815-827.
[11]	. Incorporating service scarcity and quality loss into cloud manufacturing service composition [J]. , 2021, 27(12): 3639-3650.
[12]	. Quality anomaly recognition method based on optimized probabilistic neural network [J]. , 2021, 27(10): 2813-2821.
[13]	. Block structure neighborhood search genetic algorithm for job-shop scheduling [J]. , 2021, 27(10): 2848-2857.
[14]	. Job shop scheduling with buffer constraint [J]. , 2021, 27(10): 2880-2888.
[15]	. Multi-strategy parallel genetic algorithm based on machine learning [J]. , 2021, 27(10): 2921-2928.