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.

References

References
1.
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
.
2.
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
.
3.
Xu
,
X.
,
2012
, “
From Cloud Computing to Cloud Manufacturing
,”
Rob. Comput.-Integr. Manuf.
,
28
(
1
), pp.
75
86
.
4.
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
.
5.
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
.
6.
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
.
7.
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
.
8.
Jin
,
H.
,
Yao
,
X.
, and
Chen
,
Y.
,
2015
, “
Correlation-Aware QoS Modeling and Manufacturing Cloud Service Composition
,”
J. Intell. Manuf.
(published online).
9.
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
.
10.
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
.
11.
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
.
12.
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
.
13.
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
.
14.
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
.
15.
Jula
,
A.
,
Sundararajan
,
E.
, and
Othman
,
Z.
,
2014
, “
Cloud Computing Service Composition: A Systematic Literature Review
,”
Expert Syst. Appl.
,
41
(
8
), pp.
3809
3824
.
16.
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).
17.
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
.
18.
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
.
19.
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
.
20.
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
.
21.
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
.
22.
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
.
23.
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).
24.
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
.
25.
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
.
You do not currently have access to this content.