Navigation safety improving investment aims at mitigating risk and improving safety of shipping system, while decision-makers’ attitudes toward the uncertainty of shipping safety possess a characteristic of “bounded rationality.” To study the tendency of shipping safety investment decision-making with different risk perception and appetite, a decision-making method based on cumulative prospect theory is proposed in this article. First, we extract the decision attributes through analyzing the factors affecting shipping safety investment. Then, according to cumulative prospect theory, the value function and the probability weighting function for calculating cumulative prospect values of shipping investment attributes are given. Under the risk-based multi-attribute group decision-making framework, linear programming model and projection method are introduced to aggregate the weights of attributes and decision-makers, respectively. Furthermore, through a case study, the proposed methodology is utilized in Three Gorges Dam area, and the desirable safety investment scheme is determined from a set of candidate alternatives. The case study shows not only validity and feasibility of the decision-making approach but also the mechanism of shipping safety investment decision-making with consideration of the behavior characteristics of decision-makers such as reference dependence, risk appetite distortion, and loss aversion.
MaYZhaoQXiM.Decision-makings in safety investment: an opportunity cost perspective. Safety Sci2016; 83: 31–39.
2.
ShiX.Have government regulations improved workplace safety? A test of the asynchronous regulatory effects in China’s coal industry, 1995-2006. J Safety Res2009; 40(3): 207–213.
SatoY.Optimal budget planning for investment in safety measures of a chemical company. Int J Prod Econ2012; 140(2): 579–585.
5.
BasE.An integrated quality function deployment and capital budgeting methodology for occupational safety and health as a systems thinking approach: the case of the construction industry. Accident Anal Prev2014; 68: 42–56.
6.
FengY.Effect of safety investments on safety performance of building projects. Safety Sci2013; 59: 28–45.
7.
ReniersGBrijsT.Major accident management in the process industry: an expert tool called CESMA for intelligent allocation of prevention investments. Process Saf Environ2014; 92: 779–788.
8.
Lopez-AlonsoMIbarrondo-DavilaMPRubio-GamezMCet al. The impact of health and safety investment on construction company costs. Safety Sci2013; 60: 151–159.
9.
HallowellMR.Risk-based framework for safety investment in construction organizations. J Constr Eng M ASCE2011; 137(8): 592–599.
10.
HauerE.Computing what the public wants: some issues in road safety cost–benefit analysis. Accident Anal Prev2011; 43(1): 151–164.
11.
HelleIAhtiainenHLuomaEet al. A probabilistic approach for a cost-benefit analysis of oil spill management under uncertainty: a Bayesian network model for the Gulf of Finland. J Environ Manage2015; 158: 122–132.
12.
ĆirovićGPamučarD.Decision support model for prioritizing railway level crossings for safety improvements: application of the adaptive neuro-fuzzy system. Expert Syst Appl2013; 40(6): 2208–2223.
13.
StaritaSScaparraMP.Optimizing dynamic investment decisions for railway systems protection. Eur J Oper Res2016; 248(2): 543–557.
14.
YuJLiuY.Prioritizing highway safety improvement projects: a multi-criteria model and case study withSafetyAnalyst. Safety Sci2012; 50(4): 1085–1092.
15.
ZolfaniSHEsfahaniMHBitarafanMet al. Developing a new hybrid MCDM method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents. Transport2013; 28(1): 89–96.
16.
KontovasCAPsaraftisHN.Formal safety assessment: a critical review. Mar Technol SNAME N2009; 46(1): 45–59.
MentesAAkyildizHYetkinMet al. A FSA based fuzzy DEMATEL approach for risk assessment of cargo ships at coasts and open seas of Turkey. Safety Sci2015; 79: 1–10.
19.
RuudSMikkelsenÅ.Risk-based rules for crane safety systems. Reliab Eng Syst Safe2008; 93(6): 1369–1376.
20.
PuisaRVassalosD.Robust analysis of cost-effectiveness in formal safety assessment. J Mar Sci Technol2012; 17(3): 370–381.
21.
VanemEEndresenØSkjongR.Cost-effectiveness criteria for marine oil spill preventive measures. Reliab Eng Syst Safe2008; 93(9): 1354–1368.
22.
BleichrodtHQuigginJ.Life-cycle preferences over consumption and health: when is cost-effectiveness analysis equivalent to cost-benefit analysis?J Health Econ1999; 18(6): 681–708.
23.
ChenYLiB.Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers. Sci Iran2011; 18(2): 268–274.
24.
JuYYangS.A new method for multiple attribute group decision-making with intuitionistic trapezoid fuzzy linguistic information. Soft Comput2015; 19(8): 2211–2224.
25.
LiXWangFChenX.Trapezoidal intuitionistic fuzzy multiattribute decision making method based on cumulative prospect theory and Dempster-Shafer theory. J Appl Math2014; 2014: 279138-1–279138-8.
26.
LiuSChanFTSRanW.Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl2016; 55: 37–47.
27.
SunPLiuYQiuXet al. Hybrid multiple attribute group decision-making for power system restoration. Expert Syst Appl2015; 42(19): 6795–6805.
28.
KlanacAVarstaP.Design of marine structures with improved safety for environment. Reliab Eng Syst Safe2011; 96(1): 75–90.
29.
LiDHuangZChenG.A systematic approach to heterogeneous multiattribute group decision making. Comput Ind Eng2010; 59(4): 561–572.
30.
WuBYanXPWangYet al. Selection of maritime safety control options for NUC ships using a hybrid group decision-making approach. Safety Sci2016; 88: 108–122.
31.
GurevichGKligerDLevyO.Decision-making under uncertainty—a field study of cumulative prospect theory. J Bank Financ2009; 33(7): 1221–1229.
32.
PhilippeF.Cumulative prospect theory and imprecise risk. Math Soc Sci2000; 40(3): 237–263.
33.
KahnemanDTverskyA.Prospect theory: an analysis of decision under risk. Econometrica1979; 47(2): 263–291.
34.
TverskyAKahnemanD.Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertainty1992; 5(4): 297–323.
LiuYFanZZhangY.Risk decision analysis in emergency response: a method based on cumulative prospect theory. Comput Oper Res2014; 42: 75–82.
37.
BleichrodtHSchmidtUZankH.Additive utility in prospect theory. Manage Sci2009; 55(5): 863–873.
38.
SchmidtUZankH.Risk aversion in cumulative prospect theory. Manage Sci2008; 54(1): 208–216.
39.
YangZNgAKYWangJ.A new risk quantification approach in port facility security assessment. Transport Res A: Pol2014; 59: 72–90.
40.
Valdez BandaOAGoerlandtFKuzminVet al. Risk management model of winter navigation operations. Mar Pollut Bull2016; 108(1–2): 242–262.
41.
WangSLeeC.Fuzzy multi-criteria decision-making for evaluating mutual fund strategies. Appl Econ2011; 43: 3405–3414.
42.
OpricovicSTzengGH.Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res2004; 156(2): 445–455.
43.
KouGLuYPengYet al. Evaluation of classification algorithms using MCDM and rank correlation. Int J Inf Tech Decis2012; 11(1): 197–225.
44.
ZhangWHeRXiaoQet al. Research on strategy control of taxi carpooling detour route under uncertain environment. Discrete Dyn Nat Soc2016; 2016: 4702360-1–4702360-11.
45.
FanZChenFZhangX.Method for hybrid multiple attribute decision making based on cumulative prospect theory. J Syst Eng2012; 27(3): 295–301 (in Chinese edition).
46.
KimYChungE.Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea. Appl Math Model2013; 37(22): 9419–9430.
47.
BazzaziAAOsanlooMKarimiB.Deriving preference order of open pit mines equipment through MADM methods: application of modified VIKOR method. Expert Syst Appl2011; 38(3): 2550–2556.
48.
PrelecD.The probability weighting function. Econometrica1998; 66(3): 497–527.
49.
DağdevirenMYavuzSKilincN.Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl2009; 36(4): 8143–8151.
50.
HuangJ. Combining entropy weight and TOPSIS method for information system selection. In: Proceedings of the IEEE conference on cybernetics and intelligent systems (CIS’2008), Chengdu, China, 21–24 September 2008. New York: IEEE.
51.
DongMLiSZhangH.Approaches to group decision making with incomplete information based on power geometric operators and triangular fuzzy AHP. Expert Syst Appl2015; 42(21): 7846–7857.
52.
EfeB.An integrated fuzzy multi criteria group decision making approach for ERP system selection. Appl Soft Comput2016; 38: 106–117.
53.
LiuBShenYChenYet al. A two-layer weight determination method for complex multi-attribute large-group decision-making experts in a linguistic environment. Inform Fusion2015; 23: 156–165.
54.
YueZ.Approach to group decision making based on determining the weights of experts by using projection method. Appl Math Model2012; 36(7): 2900–2910.
