With the development of cloud computing, increasing attention has shifted from traditional manufacturing toward cloud manufacturing. The distinguishing feature of cloud manufacturing is that the resource designated for a given manufacturing service is always massive, complex, and heterogeneous. Cloud manufacturing also involves a high degree of user participation and user diversity. This article presents a method of resource selection based on service evaluation, which combines predictive evaluations and recommended evaluations. Predictive evaluations are based on the user’s historical evaluations of a given service, and these evaluations may be weighted differently depending on the time when the service occurred. Recommended evaluations are given by recommenders who are selected by a two-step process. These evaluations are weighted according to their similarities and objectivities. The results of numerical experiments show that the proposed method performs better than previous methods.
BainesTSLightfootHWEvansS. (2007) State-of-the-art in product-service systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture221(10): 1543–1552.
2.
BalaAChanaI (2015) Autonomic fault tolerant scheduling approach for scientific workflows in Cloud computing. Concurrent Engineering: Research and Applications23(1): 27–39.
3.
BethTBorcherdingMKleinB (1994) Valuation of trust in open networks. In: Proceedings of the 3rd European symposium on research in computer security (ESORICS), Brighton, 7–9 November, vol. 875, pp. 1–18. Berlin, Heidelberg: Springer-Verlag.
4.
BreiterGBehrendtM (2009) Life cycle and characteristics of services in the world of cloud computing. IBM Journal of Research and Development53(4): 1–8.
5.
ChenSYLiYFKwokNM (2011) Active vision in robotic systems: A survey of recent developments. The International Journal of Robotics Research30(11): 1343–1377.
6.
DeVorREKapoorSGCaoJ. (2012) Transforming the landscape of manufacturing: Distributed manufacturing based on desktop manufacturing (DM). Journal of Manufacturing Science and Engineering: Transactions of the ASME134(4): 041004 (11 pp.).
7.
GanJDuanGJ (2012) Method of cloud manufacturing service trust evaluation. Computer Integrated Manufacturing System18(7): 1527–1535.
8.
HuYFTaoFZhouZD (2007) Manufacturing grid resource service trust-qos evaluation and application. Chinese Journal of Mechanical Engineering43(12): 203–211.
9.
HuangWChenSY (2011) Epidemic metapopulation model with traffic routing in scale-free networks. Journal of Statistical Mechanics Theory and Experiment12: P12004 (19 pp.).
10.
KimKCYooSB (2014) Collaborative design by sharing multiple-level encryption files. Concurrent Engineering: Research and Applications22(1): 29–37.
11.
LeeJYShinSJLeeYT. (2015) Toward development of a testbed for sustainable manufacturing. Concurrent Engineering: Research and Applications23(1): 64–73.
12.
LiBHZhangLChaiXD (2010a) Introduction to cloud manufacturing. ZTE Technology Journal16(4): 5–8.
13.
LiLMChenZGDengXH. (2010b) Research of subject trust comprehensive evaluation model based on fuzzy theory. Application Research of Computers27(5): 1860–1862.
14.
LiBHZhangLRenL. (2011a) Further discussion on cloud manufacturing. Computer Integrated Manufacturing Systems17(3): 449–457.
15.
LiBHZhangLWangSL. (2011b) Cloud manufacturing: A new service-oriented networked manufacturing model. Computer Integrated Manufacturing Systems16(1): 1–7; 16.
16.
LimSCEabCHMakKH. (2012) Solving linear coupled fractional differential equations by direct operational method and some applications. Mathematical Problems in Engineering 2012: 653939 (28 pp.).
17.
SanjayGShashishekaraSTMichaelS (2008) Mapping engineering design processes onto a service-grid: Turbine design optimization. Concurrent Engineering: Research and Applications16(2): 139–147.
18.
SendlerU (2013) Industrie 4.0-Beherrschung der industriellen Komplexität mit SysLM (Systems Lifecycle Management). 1st ed.Berlin, Heidelberg: Springer-Verlag.
19.
SongSSHwangKMacwanM (2004) Fuzzy trust integration for security enforcement in grid computing. In: IFIP (International Federation for Information Processing) international symposium on network and parallel computing (NPC-2004), Wuhan, China, 18–22 October, vol. 3222, pp. 9–21. Berlin, Heidelberg: Springer-Verlag.
20.
TangWChenZ (2003) Research of subjective trust management model based on the fuzzy set theory. Journal of Software14(8):1404–1408.
21.
TaoFZhangLGuoH. (2011) Typical characteristics of cloud manufacturing and several key issues of cloud service composition. Computer Integrated Manufacturing Systems17(3): 447–486.
22.
TaoFZhaoDMZhangL (2010) Resource service optimal-selection based on intuitionistic fuzzy and non-functionality QoS in manufacturing grid system. Knowledge and Information Systems25: 185–208.
23.
ViktorZAlojzijSPeterB (2008) A conceptual framework for the collaborative modeling of networked manufacturing systems. Concurrent Engineering: Research and Applications16(1): 103–114.
24.
WangHTangXQ (2006) Research on design quality evaluation system model and its construction techniques. Computer Integrated Manufacturing Systems12(1): 50–53.
25.
YangHW (2010) Cloud manufacturing: A manufacturing service. Manufacture Information Engineering of China3: 22–23.
26.
YongPByrneGCotterellM (1997) Manufacturing and the environment. The International Journal of Advanced Manufacturing Technology13: 448–493.
27.
ZhangSBLiuQZengH (2006) Fuzzy autonomous trust model based on open network. Journal of Tsinghua University (Science and Technology)46(S1): 1109–1114.
28.
ZhuLNZhaoYWWangWL (2013) A bilayer resource model of cloud manufacturing services. Mathematical Problems in Engineering2013: 607582 (10 pp.).
