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Research Papers

An Extensible Model for Multitask-Oriented Service Composition and Scheduling in Cloud Manufacturing

[+] Author and Article Information
Yongkui Liu

Center for Complex Systems,
School of Mechano-Electronic Engineering,
Xidian University,
Xi'an 710071, China;
School of Automation Science
and Electrical Engineering,
Beihang University,
Beijing 100191, China
e-mail: yongkuiliu@163.com

Xun Xu

Fellow ASME
Department of Mechanical Engineering,
The University of Auckland,
Auckland 1142, New Zealand
e-mail: xun.xu@auckland.ac.nz

Lin Zhang

School of Automation Science
and Electrical Engineering,
Beihang University,
Beijing 100191, China;
Engineering Research Center of Complex
Product Advanced Manufacturing Systems,
Ministry of Education,
Beihang University,
Beijing 100191, China
e-mail: johnlin9999@163.com

Fei Tao

School of Automation Science
and Electrical Engineering,
Beihang University,
Beijing 100191, China
e-mail: ftao@buaa.edu.cn

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 15, 2016; final manuscript received July 9, 2016; published online November 7, 2016. Assoc. Editor: Giorgio Colombo.

J. Comput. Inf. Sci. Eng 16(4), 041009 (Nov 07, 2016) (11 pages) Paper No: JCISE-16-1022; doi: 10.1115/1.4034186 History: Received January 15, 2016; Revised July 09, 2016

Cloud manufacturing is an emerging novel business paradigm for the manufacturing industry. In cloud manufacturing, distributed manufacturing resources are encapsulated into services and aggregated in a cloud manufacturing platform. Through centralized service management, cloud manufacturing is capable of dealing with multiple requirement tasks simultaneously. The ability to deal with multiple tasks at the same time is an important characteristic that distinguishes cloud manufacturing from the previous networked manufacturing models such as manufacturing grid. When it comes to multiple tasks in cloud manufacturing, a critical issue is how to schedule massive services to complete them with shortest makespan, lowest cost, and highest quality, etc. In order to facilitate the research on this issue, we in this paper propose a model for multitask-oriented service composition and scheduling in cloud manufacturing, in which key factures of cloud manufacturing such as service orientation, involvement of logistics, and dynamical change of service availability are taken into account. New concepts such as service efficiency, enterprise capability, and task workload are introduced, and various types of times including service time, logistics time, and waiting time are analyzed in detail. Moreover, this model can be conveniently extended by incorporating new elements such as task constraints, task priority, and continuous task arrival. An example that motivates the current model is presented. Simulation experiments with different numbers of tasks are performed to demonstrate the feasibility of the model.

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References

Li, B. H. , Zhang, L. , Wang, S. L. , Tao, F. , Cao, J. W. , Jiang, X. D. , Song, X. , and Chai, X. D. , 2010, “ Cloud Manufacturing: A New Service-Oriented Manufacturing Model,” Comput. Integr. Manuf. Syst., 16(1), pp. 1–8.
Zhang, L. , Luo, Y. L. , Tao, F. , Li, B. H. , Ren, L. , Zhang, X. S. , Guo, H. , Cheng, Y. , Hu, A. R. , and Liu, Y. K. , 2014, “ Cloud Manufacturing: A New Manufacturing Paradigm,” Enterp. Inf. Syst., 8(2), pp. 167–187. [CrossRef]
Xu, X. , 2012, “ From Cloud Computing to Cloud Manufacturing,” Rob. Comput.-Integr. Manuf., 28(1), pp. 75–86. [CrossRef]
Jian, C. F. , and Wang, Y. , 2014, “ Batch Task Scheduling-Oriented Optimization Modelling and Simulation in Cloud Manufacturing,” Int. J. Simul. Modell., 13(1), pp. 93–101. [CrossRef]
Cheng, Z. , Zhan, D. , Zhao, X. , and Wan, H. , 2014, “ Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing,” J. Appl. Math., 2014, p. 369350.
Tao, F. , LaiLi, Y. , Xu, L. , and Zhang, L. , 2013, “ FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System,” IEEE Trans. Ind. Inf., 9(4), pp. 2023–2033. [CrossRef]
Lartigau, J. , Xu, X. , Nie, L. , and Zhan, D. , 2015, “ Cloud Manufacturing Service Composition Based on QoS With Geo-Perspective Transportation Using an Improved Artificial Bee Colony Optimisation Algorithm,” Int. J. Prod. Res., 53(14), pp. 4380–4404. [CrossRef]
Jin, H. , Yao, X. , and Chen, Y. , 2015, “ Correlation-Aware QoS Modeling and Manufacturing Cloud Service Composition,” J. Intell. Manuf. (published online).
Cao, Y. , Wang, S. , Kang, L. , and Cao, Y. , 2016, “ A TQCS-Based Service Selection and Scheduling Strategy in Cloud Manufacturing,” Int. J. Adv. Manuf. Technol., 82(1), pp. 235–251. [CrossRef]
Liu, W. N. , Liu, B. , and Sun, D. H. , 2013, “ Multi-Task Oriented Service Composition in Cloud Manufacturing,” Comput. Integr. Manuf. Syst., 19(1), pp. 199–209.
Liu, W. , Liu, B. , Sun, D. , Li, Y. , and Ma, G. , 2013, “ Study on Multi-Task Oriented Services Composition and Optimisation With the ‘Multi-Composition for Each Task' Pattern in Cloud Manufacturing Systems,” Int. J. Comput. Integr. Manuf., 26(8), pp. 786–805. [CrossRef]
Lartigau, J. , Nie, L. , Xu, X. , and Mou, T. , 2012, “ Scheduling Methodology for Production Services in Cloud Manufacturing,” International Joint Conference on Service Sciences (IJCSS), Shanghai, China, May 24–26, pp. 34–39.
Duflou, J. R. , Sutherland, J. W. , Dornfeld, D. , Herrmannd, C. , Jeswiete, J. , Karaf, S. , Hauschildg, M. , and Kellensa, K. , 2012, “ Towards Energy and Resource Efficient Manufacturing: A Processes and Systems Approach,” CIRP Ann.-Manuf. Technol., 61(2), pp. 587–609. [CrossRef]
Vidayev, I. G. , Martyushev, N. , Ivashutenko, A. S. , and Bogdan, A. M. , 2014, “ The Resource Efficiency Assessment Technique for the Foundry Production,” Adv. Mater. Res., 880, pp. 141–145. [CrossRef]
Jula, A. , Sundararajan, E. , and Othman, Z. , 2014, “ Cloud Computing Service Composition: A Systematic Literature Review,” Expert Syst. Appl., 41(8), pp. 3809–3824. [CrossRef]
Liu, Y. , Zhang, L. , Tao, F. , and Wang, L. , 2015, “ Resource Service Sharing in Cloud Manufacturing Based on the Gale–Shapley Algorithm: Advantages and Challenge,” Int. J. Comput. Integr. Manuf. (published online).
Renna, P. , and Argoneto, P. , 2016, “ Supporting Capacity Sharing in the Cloud Manufacturing Environment Based on Game Theory and Fuzzy Logic,” Enterp. Inf. Syst., 10(2), pp. 193–210. [CrossRef]
Kumar, P. , and Verma, A. , 2012, “ Scheduling Using Improved Genetic Algorithm in Cloud Computing for Independent Tasks,” International Conference on Advances in Computing, Communications and Informatics, Chennai, India, Aug. 3–5, ACM, New York, NY, pp. 137–142.
Wu, X. , Deng, M. , Zhang, R. , Zeng, B. , and Zhou, S. , 2013, “ A Task Scheduling Algorithm Based on QoS-Driven in Cloud Computing,” Procedia Comput. Sci., 17, pp. 1162–1169. [CrossRef]
Li, W. , Zhu, C. , Yang, L. T. , Shu, L. , Ngai, E. C. H. , and Ma, Y. , 2015, “ Subtask Scheduling for Distributed Robots in Cloud Manufacturing,” IEEE Syst. J., PP(99), pp. 1–10.
Wei, Y. , and Tian, L. , 2012, “ Research on Cloud Design Resources Scheduling Based on Genetic Algorithm,” International Conference on Systems and Informatics (ICSAI), Yantai, China, May 19–20, pp. 2651–2656.
Laili, Y. , Zhang, L. , and Tao, F. , 2011, “ Energy Adaptive Immune Genetic Algorithm for Collaborative Design Task Scheduling in Cloud Manufacturing System,” IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, Dec. 6–9, pp. 1912–1916.
Lin, Y. K. , and Chong, C. S. , 2015, “ Fast GA-Based Project Scheduling for Computing Resources Allocation in a Cloud Manufacturing System,” J. Intell. Manuf. (published online).
Cheng, Y. , Tao, F. , Liu, Y. , Zhao, D. , Zhang, L. , and Xu, L. , 2013, “ Energy-Aware Resource Service Scheduling Based on Utility Evaluation in Cloud Manufacturing System,” Proc. Inst. Mech. Eng., Part B, 227(12), pp. 1901–1915. [CrossRef]
Yi, S. , Tan, M. , Guo, Z. , Wen, P. , and Zhou, J. , 2015, “ Manufacturing Task Decomposition Optimization in Cloud Manufacturing Service Platform,” Comput. Integr. Manuf. Syst., 16(1), pp. 1–7.

Figures

Grahic Jump Location
Fig. 1

Illustration of the searching and matching process between task Tk and services offered by nine enterprises (i.e., E1– E9)

Grahic Jump Location
Fig. 2

A service scheduling example geared toward ten tasks (numbering from 1 to 10). The vertical axis displays the service types (numbering from 1 to 10) of the services offered by ten enterprises (Ent.) (i.e., E1 – E10) and the horizontal axis shows the time in terms of period p. In this example, each enterprise offers four different types (Ser. Type) of services and each type of service is offered by four different enterprises.

Grahic Jump Location
Fig. 3

Diagram of service scheduling with I=10, li=4, J=10, K=10, wT=0.4, wC=0.3, and wRel=0.3. For this scheduling diagram, AT=18.5, AC=4356.7, AP=0.8507, and SU=0.1707.

Grahic Jump Location
Fig. 4

Diagram of service scheduling with I=10,  li=4, J=10, K=50, wT=0.4, wC=0.3, and wRel=0.3. For this scheduling diagram, AT=27.6, AC=4486.78, AP=0.8368, and SU=0.4988.

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