In order to achieve optimal selection of manufacturing services in cloud manufacturing environment, this paper proposed a novel hybrid MCDM approach integrated Rough Analytic Network Process (rough ANP) and Rough Technique for Order of Preference by Similarity to Ideal Solution (rough TOPSIS). In the first step, the novel evaluation method based on rough-ANP is proposed to determine weight of each indicator. In the second step, the decision-making system based on rough-TOPSIS is developed to compare and rank alternatives. At the same time, in group decision-making process, the concepts of rough number and rough boundary are introduced to express more integrated information. The novel approach makes use of the strength of rough set theory in handling vagueness and uncertainty, the superiority of Analytic Network Process (ANP) in non-independent hierarchy evaluation and the advantage of TOPSIS in multiple-objective decision analysis. Finally, a case study is presented to demonstrate the practicability and validity of the novel approach.
RobinsonB.H., E-waste: An assessment of global production and environmental impacts, Sci Total Environ408(2) (2009), 183–191.
2.
XuX., From cloud computing to cloud manufacturing, Robot Comput Integr Manuf28(1) (2012), 75–86.
3.
HeW. and XuL., A state-of-the-art survey of cloud manufacturing, Int J Comput Integr Manuf28(3) (2014), 239–250.
4.
ValilaiO.F. and HoushmandM., A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm, Robot Comput Integr Manuf29(1) (2013), 110–127.
5.
HeloP., SuorsaM., HaoY. and AnussornnitisarnP., Toward a cloud-based manufacturing execution system for distributed manufacturing, Comput Ind65(4) (2014), 646–656.
6.
ZhanD.C., et al., Cloud manufacturing service platform for group enterprises oriented to manufacturing and management, Jisuanji Jicheng Zhizao Xitong/Computer Integr Manuf Syst CIMS17(3) (2011), 487–494.
7.
TaiL.J., HuR.F., ChenC.W. and HuangY.D., Manufacturing resources and demand intelligent matching in cloud manufacturing environment, in vol. 616, pp, Advanced Materials Research (2012), 2101–2104.
8.
ShengB.Y., ZhangC.L., LuQ.B., LiX.L. and ChengX.D., Research and implementation of cloud manufacturing service platform’s intelligent matching for supply and demand, Jisuanji Jicheng Zhizao Xitong/Computer Integr Manuf Syst CIMS21(3) (2015), 822–830.
9.
SivakumarK., BalamuruganC. and RamabalanS., Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection, CAD Comput Aided Des43(2) (2011), 207–218.
10.
HuangS., ZengS., FanY. and HuangG.Q., Optimal service selection and composition for service-oriented manufacturing network, Int J Comput Integr Manuf24(5) (2011), 416–430.
11.
JuY., WangA. and LiuX., Evaluating emergency response capacity by fuzzy AHP and 2-tuple fuzzy linguistic approach, Expert Syst Appl39(8) (2012), 6972–6981.
12.
ZhuG.N., HuJ., QiJ., GuC.C. and PengY.H., An integrated AHP and VIKOR for design concept evaluation based on rough number, Adv Eng Informatics29(3) (2015), 408–418.
13.
SongW., MingX. and WuZ., An integrated rough number-based approach to design concept evaluation under subjective environments, J Eng Des24(5) (2013), 320–341.
14.
BrinthaN.C., JappesJ.T.W. and BenedictS., A Modified Ant Colony based optimization for managing Cloud resources in manufacturing pp, 2016 2nd Int Conf Green High Perform Comput ICGHPC 2016 (2016), 0–5sector.
15.
ChengY., TaoF., ZhaoD. and ZhangL., Modeling of manufacturing service supply—demand matching hypernetwork in service-oriented manufacturing systems, Robot Comput Integr Manuf45 (2017), 59–72.
16.
ZhengH., FengY. and TanJ., A hybrid energy-aware resource allocation approach in cloud manufacturing environment, IEEE Access5 (2017).
17.
ZhengH., FengY. and TanJ., A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system, Int J Adv Manuf Technol84(1-4) (2016), 371–379.
18.
TranV.X., TsujiH. and MasudaR., A new QoS ontology and its QoS-based ranking algorithm for Web services, Simul Model Pract Theory17(8) (2009), 1378–1398.
19.
ZadehL.A., “FUZZY SETS,” in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems, vol. Volume 6, WORLD SCIENTIFIC, pp, (1996), 394–432.
20.
WangY.M., LuoY. and LiangL., Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises, Expert Syst Appl36(3) pp. 5205–5211 PART 1.
21.
HaqA.N. and KannanG., Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model, Int J Adv Manuf Technol29(7–8) (2006), 826–835.
22.
KumruM. and KumruP.Y., A fuzzy ANP model for the selection of 3D coordinate-measuring machine, J Intell Manuf26(5) (2015), 999–1010.
23.
IzadikhahM., SaeidifarA. and RoostaeeR., Extending TOPSIS in fuzzy environment by using the nearest weighted interval approximation of fuzzy numbers, J Intell Fuzzy Syst27(6) (2014), 2725–2736.
24.
PawlakZ., Rough set theory and its applications, J Telecommun Inf Technol29(7) (1998), 7–10.
25.
SunB., GongZ. and ChenD., Fuzzy rough set theory for the interval-valued fuzzy information systems, Inf Sci (Ny)178(13) (2008), 2794–2815.
26.
BaiC. and SarkisJ., Integrating sustainability into supplier selection with grey system and rough set methodologies, Int J Prod Econ124(1) (2010), 252–264.
27.
SongW., MingX., WuZ. and ZhuB., A rough TOPSIS approach for failure mode and effects analysis in uncertain environments, Qual Reliab Eng Int30(4) (2014), 473–486.
28.
ZhaiL.-Y., KhooL.-P. and ZhongZ.-W., A rough set enhanced fuzzy approach to quality function deployment, Int J Adv Manuf Technol37(5–6) (2007), 613–624.
29.
ZhaiL., KhooL. and ZhongZ., A dominance-based rough set approach to Kansei Engineering in product development, Expert Syst Appl36(1) (2009), 393–402.
30.
Gölcükİ. and Baykasoğlu A., , An analysis of DEMATEL approaches for criteria interaction handling within ANP, Expert Syst Appl46 (2016), 346–366.
31.
KayaT. and KahramanC., Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology, Expert Syst Appl38(6) (2011), 6577–6585.
32.
SaatyT.L., The analytic hierarchy process: What it is and how it is used, Math Model9(3) (1987), 161–176.
