The supplier selection is a key component of the supply chain management. Existing methods for the supplier selection are based on analytic network process. They can handle the interdependence of decision attributes; however, these methods could not guarantee an optimal solution when given vague or incomplete input data. To deal with the uncertainties of input data, we propose methods combining analytic network process with Dempster–Shafer evidence theory. We demonstrate efficiency and accuracy of the proposed method in a numerical example. We demonstrate that the proposed method is flexible and effective in dealing with the supplier selection problem.
AlsyoufIAl-AraidahOTatesM. A multi-criteria decision-making framework for assessing the quality and cost of facility layout alternatives: a case study. Proc IMechE, Part B: J Engineering Manufacture2012; 226(2): 353–364.
2.
PengJPYehWCLaiTC. Similarity-based method for multiresponse optimization problems with intuitionistic fuzzy sets. Proc IMechE, Part B: J Engineering Manufacture2013; 227(6): 908–916.
3.
ChanFTSKumarN. Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega2007; 35(4): 417–431.
4.
ZhangHDengYChanFT. A modified multi-criterion optimization genetic algorithm for order distribution in collaborative supply chain. Appl Math Model2013; 37(14): 7855–7864.
5.
ChenCCShihHSShyurHJ. A business strategy selection of green supply chain management via an analytic network process. Comp Math Appl2012; 64(8): 2544–2557.
6.
ChakladarNDChakrabortyS. A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection. Proc IMechE, Part B: J Engineering Manufacture2008; 222(12): 1613–1623.
7.
ShidpourHShahrokhiMBernardA. A multi-objective programming approach, integrated into the TOPSIS method, in order to optimize product design; in three-dimensional concurrent engineering. Comput Ind Eng2013; 63(4): 875–885.
8.
ChenJZhangZTianX. An approach to partner selection in virtual enterprises based on grey relational analysis. Proc IMechE, Part B: J Engineering Manufacture2011; 225(12): 2296–2301.
9.
SaatyTL. The analytic hierarchy process. New York: McGraw-Hill, 1980.
10.
SaatyTL. How to make a decision: the analytic hierarchy process. Eur J Oper Res1990; 48(1): 9–26.
11.
SaatyTL. The modern science of multicriteria decision making and its practical applications: the AHP/ANP approach. Oper Res2013; 61(5): 1101–1118.
12.
SaatyTL. Analytic network process. In: GassSIHarrisCM (eds) Encyclopedia of operations research and management science. New York: Springer, 2001, pp.28–35.
13.
GencerCGürpinarD. Analytic network process in supplier selection: a case study in an electronic firm. Appl Math Model2007; 31(11): 2475–2486.
14.
YükselIDagdevirenM. Using the analytic network process (ANP) in a SWOT analysis—a case study for a textile firm. Inform Sci2007; 177(16): 3364–3382.
15.
TsengML. Green supply chain management with linguistic preferences and incomplete information. Appl Soft Comput2011; 11(8): 4894–4903.
16.
KangHYLeeAHI. Inventory replenishment model using fuzzy multiple objective programming: a case study of a high-tech company in Taiwan. Appl Soft Comput2010; 10(4): 1108–1118.
17.
ChanFChanH. Development of the supplier selection model—a case study in the advanced technology industry. Proc IMechE, Part B: J Engineering Manufacture2004; 218(12): 1807–1824.
18.
FlynnBBHuoBZhaoX. The impact of supply chain integration on performance: a contingency and configuration approach. J Oper Manag2010; 28(1): 58–71.
19.
DempsterAP. Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat1967; 38: 325–339.
20.
ShaferG. A mathematical theory of evidence, vol. 1. Princeton, NJ: Princeton University Press, 1976.
21.
FaouziNEELeungHKurianA. Data fusion in intelligent transportation systems: progress and challenges—a survey. Inform Fusion2011; 12(1): 4–10.
22.
LeungYJiNNMaJH. An integrated information fusion approach based on the theory of evidence and group decision-making. Inform Fusion2013; 14(4): 410–422.
23.
LiuZPanQDezertJ. Evidential classifier for imprecise data based on belief functions. Knowl-Based Syst2013; 52: 246–257.
24.
KumarPMittalAKumarP. Addressing uncertainty in multi-modal fusion for improved object detection in dynamic environment. Inform Fusion2010; 11(4): 311–324.
25.
FriguiHZhangLGaderP. An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination. Inform Fusion2012; 13(2): 161–174.
26.
LiuZPanQDezertJ. A belief classification rule for imprecise data. Appl Intell2013; 40(2): 214–228.
LiuZPanQDezertJ. Credal classification rule for uncertain data based on belief functions. Pattern Recogn2014; 47(7): 2532–2541.
29.
WeiDDengXZhangX. Identifying influential nodes in weighted networks based on evidence theory. Physica A2013; 392(10): 2564–2575.
30.
ZhangCHuYChanFTS. A new method to determine basic probability assignment using core samples. Knowl-Based Syst. Epub ahead of print 8 July 2014. DOI: 10.1016/j.knosys.2014.06.015.
31.
IshizakaAPearmanCNemeryP. AHPSort: an AHP-based method for sorting problems. Int J Prod Res2012; 50(17): 4767–4784.
32.
IshizakaABalkenborgDKaplanT. Influence of aggregation and measurement scale on ranking a compromise alternative in AHP. J Oper Res Soc2011; 62(4): 700–710.
33.
IshizakaALabibA. Review of the main developments in the analytic hierarchy process. Expert Syst Appl2011; 38(11): 14336–14345.
34.
ChanFS. Interactive selection model for supplier selection process: an analytical hierarchy process approach. Int J Prod Res2003; 41(15): 3549–3579.
35.
YangCCChenBS. Supplier selection using combined analytical hierarchy process and grey relational analysis. J Manuf Tech Manag2006; 17(7): 926–941.
36.
BeiWWangSHuJ. An analysis of supplier selection in manufacturing supply chain management. In: 2006 international conference on service systems and service management, vol. 2, Troyes, 25–27 October 2006, pp.1439–1444. New York: IEEE.
37.
ChanFTSKumarNTiwariM. Global supplier selection: a fuzzy-AHP approach. Int J Prod Res2008; 46(14): 3825–3857.
38.
LeeAH. A fuzzy AHP evaluation model for buyer–supplier relationships with the consideration of benefits, opportunities, costs and risks. Int J Prod Res2009; 47(15): 4255–4280.
39.
ShawKShankarRYadavSS. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Syst Appl2012; 39(9): 8182–8192.
40.
DengXHuYDengY. Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl2014; 41(1): 156–167.
41.
DengXHuYDengY. Environmental impact assessment based on D numbers. Expert Syst Appl2014; 41(2): 635–643.
42.
SarkisJTalluriS. A model for strategic supplier selection. J Supply Chain Manag2002; 38(1): 18–28.
43.
WuWYSukocoBMLiCY. An integrated multi-objective decision-making process for supplier selection with bundling problem. Expert Syst Appl2009; 36(2): 2327–2337.
44.
VinodhSAnesh RamiyaRGauthamS. Application of fuzzy analytic network process for supplier selection in a manufacturing organisation. Expert Syst Appl2011; 38(1): 272–280.
45.
KuoRLinY. Supplier selection using analytic network process and data envelopment analysis. Int J Prod Res2012; 50(11): 2852–2863.
46.
DouYZhuQSarkisJ. Evaluating green supplier development programs with a grey-analytical network process-based methodology. Eur J Oper Res2014; 233(2): 420–431.
47.
ÖnütSKaraSSIşikE. Long term supplier selection using a combined fuzzy MCDM approach: a case study for a telecommunication company. Expert Syst Appl2009; 36(2): 3887–3895.
48.
GuoXZhuZShiJ. Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection. Expert Syst Appl2014; 41(4): 2083–2097.
49.
Nazari-ShirkouhiSShakouriHJavadiB. Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming. Appl Math Model2013; 37(22): 9308–9323.
50.
UtkinLV. A new ranking procedure by incomplete pairwise comparisons using preference subsets. Intell Data Anal2009; 13(2): 229–241.
51.
UtkinLVSimanovaNV. Multi-criteria decision making by incomplete preferences. J Uncertain Syst2008; 2(4): 255–266.
52.
ZhangYDengXWeiD. Assessment of E-Commerce security using AHP and evidential reasoning. Expert Syst Appl2012; 39(3): 3611–3623.
53.
LiuJChanFTLiY. A new optimal consensus method with minimum cost in fuzzy group decision. Knowl-Based Syst2012; 35: 357–360.
54.
IshizakaANguyenNH. Calibrated fuzzy AHP for current bank account selection. Expert Syst Appl2013; 40(9): 3775–3783.
55.
ZhangXDengYChanFT. A novel methodology for the job-shop scheduling problem based on intuitionistic fuzzy sets. Int J Prod Res2013; 51(17): 5100–5119.
56.
HuangSSuXHuY. A new decision-making method by incomplete preferences based on evidence distance. Knowl-Based Syst2013; 56: 264–272.
57.
ZhangYZhangZDengY. A biologically inspired solution for fuzzy shortest path problems. Appl Soft Comp2013; 13(5): 2356–2363.
58.
ChenSDengYWuJ. Fuzzy sensor fusion based on evidence theory and its application. Appl Artif Intell2013; 27(3): 235–248.
ChenCTLinCTHuangSF. A fuzzy approach for supplier evaluation and selection in supply chain management. Int J Prod Econ2006; 102(2): 289–301.
61.
ChaiJLiuJNXuZ. A new rule-based SIR approach to supplier selection under intuitionistic fuzzy environments. Int J Unc: Fuzz Knowl-Based Syst2012; 20(3): 451–471.
62.
ChouSYChangYH. A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Syst Appl2008; 34(4): 2241–2253.
63.
WuDD. Supplier selection in a fuzzy group setting: a method using grey related analysis and Dempster–Shafer theory. Expert Syst Appl2009; 36(5): 8892–8899.
64.
DulminRMininnoV. Supplier selection using a multi-criteria decision aid method. J Purch Supply Manag2003; 9(4): 177–187.
65.
AminSHRazmiJZhangG. Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Syst Appl2011; 38(1): 334–342.
66.
TaoLChenYLiuX. An integrated multiple criteria decision making model applying axiomatic fuzzy set theory. Appl Math Model2012; 36(10): 5046–5058.
67.
BasirOYuanX. Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inform Fusion2007; 8(4): 379–386.
68.
BeynonMCurryBMorganP. The Dempster–Shafer theory of evidence: an alternative approach to multicriteria decision modelling. Omega2000; 28(1): 37–50.
69.
UtkinLVSimanovaNV. The DS/AHP method under partial information about criteria and alternatives by several levels of criteria. Int J Inf Tech Decis2012; 11(2): 307–326.
70.
BeynonM. DS/AHP method: a mathematical analysis, including an understanding of uncertainty. Eur J Oper Res2002; 140(1): 148–164.
71.
AndersonDR. An introduction to management science: quantitative approaches to decision making. Boston, MA: Cengage Learning, 2012.
