Full Text:   <1874>

Summary:  <1449>

CLC number: TP27

On-line Access: 2019-12-10

Received: 2019-02-19

Revision Accepted: 2019-10-24

Crosschecked: 2019-11-28

Cited: 0

Clicked: 5675

Citations:  Bibtex RefMan EndNote GB/T7714


Yong-kui Liu


Xue-song Zhang


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.11 P.1465-1492


A multi-agent architecture for scheduling in platform-based smart manufacturing systems

Author(s):  Yong-kui Liu, Xue-song Zhang, Lin Zhang, Fei Tao, Li-hui Wang

Affiliation(s):  Center for Intelligent Manufacturing Systems and Robots, School of Mechano-Electronic Engineering, Xidian University, Xian 710071, China; more

Corresponding email(s):   yongkuiliu@163.com, xs_zhang@126.com, zhanglin@buaa.edu.cn, ftao@buaa.edu.cn, lihuiw@kth.se

Key Words:  Platform, Smart manufacturing, Multi-agent, Scheduling

Yong-kui Liu, Xue-song Zhang, Lin Zhang, Fei Tao, Li-hui Wang. A multi-agent architecture for scheduling in platform-based smart manufacturing systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(11): 1465-1492.

@article{title="A multi-agent architecture for scheduling in platform-based smart manufacturing systems",
author="Yong-kui Liu, Xue-song Zhang, Lin Zhang, Fei Tao, Li-hui Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A multi-agent architecture for scheduling in platform-based smart manufacturing systems
%A Yong-kui Liu
%A Xue-song Zhang
%A Lin Zhang
%A Fei Tao
%A Li-hui Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 11
%P 1465-1492
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900094

T1 - A multi-agent architecture for scheduling in platform-based smart manufacturing systems
A1 - Yong-kui Liu
A1 - Xue-song Zhang
A1 - Lin Zhang
A1 - Fei Tao
A1 - Li-hui Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 11
SP - 1465
EP - 1492
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900094

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise- level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Akbaripour H, Houshmand M, van Woensel T, et al., 2018. Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed- integer programming models. Int J Adv Manuf Technol, 95(1-4):43-70.

[2]Albert R, Barabási AL, 2002. Statistical mechanics of complex networks. Rev Mod Phys, 74(1):47-97.

[3]Cai NX, Wang LH, Feng HY, 2009. GA-based adaptive setup planning toward process planning and scheduling integration. Int J Prod Res, 47(10):2745-2766.

[4]Cao Y, Wang SL, Kang L, et al., 2016. A TQCS-based service selection and scheduling strategy in cloud manufacturing. Int J Adv Manuf Technol, 82(1-4):235-251.

[5]Chekired DA, Khoukhi L, Mouftah HT, 2018. Industrial IoT data scheduling based on hierarchical fog computing: a key for enabling smart factory. IEEE Trans Ind Inform, 14(10): 4590-4602.

[6]Cheng Z, Zhan DC, Zhao XB, et al., 2014. Multitask oriented virtual resource integration and optimal scheduling in cloud manufacturing. J Appl Math, 2014:369350.

[7]Choi K, Chung SH, 2017. Enhanced time-slotted channel hopping scheduling with quick setup time for Industrial Internet of Things networks. Int J Dis Sens Netw, 13(6): 1-14.

[8]Evans PC, Annunziata M, 2012. Industrial Internet: Pushing the Boundaries of Minds and Machines. GE.

[9]Fu YP, Ding JL, Wang HF, et al., 2018. Two-objective stochastic flow-shop scheduling with deteriorating and learning effect in Industry 4.0-based manufacturing system. Appl Soft Comput, 68:847-855.

[10]Ivanov D, Dolgui A, Sokolov B, et al., 2016. A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. Int J Prod Res, 54(2): 386-402.

[11]Kagermann H, Helbig J, Hellinger A, et al., 2013. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group. Forschungsunion.

[12]Kang HS, Lee JY, Choi S, et al., 2016. Smart manufacturing: past research, present findings, and future directions. Int J Prec Eng Manuf-Green Technol, 3(1):111-128.

[13]Laili Y, Zhang L, Tao F, 2011. Energy adaptive immune genetic algorithm for collaborative design task scheduling in cloud manufacturing system. IEEE Int Conf on Industrial Engineering and Engineering Management, p.1912- 1916.

[14]Lartigau J, Nie LS, Xu XF, et al., 2012. Scheduling methodology for production services in cloud manufacturing. Int Joint Conf on Service Sciences, p.34-39.

[15]Lartigau J, Xu XF, Zhan DC, 2014. Artificial bee colony optimized scheduling framework based on resource service availability in cloud manufacturing. Int Conf on Service Sciences, p.181-186.

[16]Li JS, Wang AM, Tang CT, et al., 2012. Distributed coordination scheduling technology based on dynamic manufacturing ability service. Comput Integr Manuf Syst, 18(7):1563-1574 (in Chinese).

[17]Li K, Zhang HJ, Cheng BY, et al., 2018. Uniform parallel machine scheduling problems with fixed machine cost. Optim Lett, 12(1):73-86.

[18]Li WX, Zhu CS, Yang LT, et al., 2017. Subtask scheduling for distributed robots in cloud manufacturing. IEEE Syst J, 11(2):941-950.

[19]Lin YK, Chong CS, 2017. Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J Int Manuf, 28(5):1189-1201.

[20]Liu YK, Xu X, 2017. Industry 4.0 and cloud manufacturing: a comparative analysis. J Manuf Sci Eng Mar, 139(3): 034701.

[21]Liu YK, Xu X, Zhang L, et al., 2016. An extensible model for multitask-oriented service composition and scheduling in cloud manufacturing. J Comput Inform Sci Eng, 16(4): 041009.

[22]Liu YK, Xu X, Zhang L, et al., 2017. Workload-based multi- task scheduling in cloud manufacturing. Robot Comput Integr Manuf, 45:3-20.

[23]Liu YK, Wang LH, Wang YQ, et al., 2018. Multi-agent-based scheduling in cloud manufacturing with dynamic task arrivals. Proc CIRP, 72:953-960.

[24]Liu YK, Wang LH, Wang XV, et al., 2019. Scheduling in cloud manufacturing: state-of-the-art and research challenges. Int J Prod Res, 57(15-16):4854-4879.

[25]Lu JS, Hu QH, Dong QY, et al., 2017. Cloud manufacturing- oriented mixed-model hybrid shop-scheduling problem. China Mech Eng, 28(2):191-198, 205 (in Chinese).

[26]Ma J, Luo GF, Lu D, et al., 2014. Research on manufacturing resource cloud integration meta modeling and cloud- agent service scheduling. China Mech Eng, 25(7):917- 923, 930 (in Chinese).

[27]Macchiaroli R, Riemma S, 2002. A negotiation scheme for autonomous agents in job shop scheduling. Int J Comput Integr Manuf, 15(3):222-232.

[28]Mourtzis D, Vlachou E, 2018. A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition- based maintenance. J Manuf Syst, 47:179-198.

[29]Mourtzis D, Vlachou E, Doukas M, et al., 2015. Cloud-based adaptive shop-floor scheduling considering machine tool availability. ASME Int Mechanical Engineering Congress and Exposition, p.13-19.

[30]Ojo M, Giordano S, Adami D, et al., 2018. A novel auction based scheduling algorithm in Industrial Internet of Things networks. Int Conf on Computer Networks, p.103- 114.

[31]Ouelhadj D, Petrovic S, 2009. A survey of dynamic scheduling in manufacturing systems. J Sched, 12(4):417-431.

[32]Qiu T, Qiao RX, Wu DO, 2018. EABS: an event-aware backpressure scheduling scheme for emergency Internet of Things. IEEE Trans Mob Comput, 17(1):72-84.

[33]Shen WM, 2002. Distributed manufacturing scheduling using intelligent agents. IEEE Intell Syst, 17(1):88-94.

[34]Shen WM, Wang LH, Hao Q, 2006. Agent-based distributed manufacturing process planning and scheduling: a state- of-the-art survey. IEEE Trans Syst Man Cybern Part C, 36(4):563-577.

[35]Tai LJ, Hu RF, Zhao H, et al., 2013. Multi-objective dynamic scheduling of manufacturing resource to cloud manufacturing services. China Mech Eng, 24(12):1616-1622 (in Chinese).

[36]Tang CG, Wei XL, Xiao S, et al., 2018. A mobile cloud based scheduling strategy for industrial Internet of Things. IEEE Access, 6:7262-7275.

[37]Wang LH, Haghighi A, 2016. Combined strength of holons, agents and function blocks in cyber-physical systems. J Manuf Syst, 40:25-34.

[38]Wang LH, Shen WM, 2007. Process Planning and Scheduling for Distributed Manufacturing. Springer-Verlag, London, UK.

[39]Wang Z, Zhang JH, Qi YQ, 2017. Job shop scheduling method with idle time in cloud manufacturing. Contr Dec, 32(5):811-816 (in Chinese).

[40]Wong TN, Leung CW, Mak KL, et al., 2006. Dynamic shopfloor scheduling in multi-agent manufacturing systems. Expert Syst Appl, 31(3):486-494.

[41]Xiang W, Lee HP, 2008. Ant colony intelligence in multi- agent dynamic manufacturing scheduling. Eng Appl Artif Intell, 21(1):73-85.

[42]Xiao YY, Li BH, Zhuang CH, et al., 2015. Distributed supply chain scheduling oriented to multi-variety customization. Comput Integr Manuf Syst, 21(3):800-812 (in Chinese).

[43]Xiao YY, Li BH, Hou BC, et al., 2016. Planning and scheduling technology review of supply chain management in smart manufacturing cloud. Comput Integr Manuf Syst, 22(7):1619-1635 (in Chinese).

[44]Yuan MH, Deng K, Chaovalitwongse WA, et al., 2017. Multi- objective optimal scheduling of reconfigurable assembly line for cloud manufacturing. Optim Methods Softw, 32(3):581-593.

[45]Zhang J, Wang XX, 2016. Multi-agent-based hierarchical collaborative scheduling in re-entrant manufacturing systems. Int J Prod Res, 54(23):7043-7059.

[46]Zhang L, Luo YL, Tao F, et al., 2014. Cloud manufacturing: a new manufacturing paradigm. Enterpr Inform Syst, 8(2): 167-187.

[47]Zhang SC, Wong TN, 2017. Flexible job-shop scheduling/ rescheduling in dynamic environment: a hybrid MAS/ ACO approach. Int J Prod Res, 55(11):3173-3196.

[48]Zhang YF, Xi D, Yang HD, et al., 2017a. Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine. J Intell Manuf, 30(7):2681-2699.

[49]Zhang YF, Wang J, Liu SC, et al., 2017b. Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing. Int J Intell Syst, 32(4):437-463.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE