We present a methodology and an example of preparing an order of merit list to rank terrorist targets based upon decision-maker weights. We used an old terrorist data set as our base data to keep the information unclassified. This data is used to demonstrate this methodology. We perform numerical iterative criteria weight sensitivity analysis to show the effects on the model’s outputs in changes in the weights. We identify the critical criterion.
TwedellPEdmondsW. Countermetrics System (CMS): Using Data and Comparative Evidence to Improve Human-Targeting Decisions. Master’s thesis, Naval Postgraduate School, 2008.
MalczewskiJ. A GIS approach to multiple criteria group decision making. Int J Geographic Inform Syst. 1996, 10(8), 955–971.
7.
NapierJ. Industrial base program item selection indicators analytical enhancements. Department of Defense Pamphlet, DLA-93-P20047, 1992.
8.
FoxWP. Mathematical modeling of the analytical hierarchy process using discrete dynamical systems in decision analysis. Comput Ed J2012; (July–September): 27–34.
9.
FoxWPEvertonSF. Using data envelopment analysis and the analytical hierarchy process to find node influences in a social network. JDMS2014. DOI: 10.1177/15485129 13518273.
10.
FoxWEvertonS. Mathematical modeling in social network analysis: using TOPSIS to find node influences in a social network. J Math Syst Sci2013; 3(10): 531–541.
11.
WangYHeZ. Improved TOPSIS methods for multi-response optimization. In IEEE Symposium on Advanced Management of Information for Globalized Enterprises, 2008 (AMIGE 2008).
12.
OzturkDBatukF. Technique for order preference by similarity to ideal solution (TOPSIS) for spatial decision problem. In: Proceedings ISPRS2011, retrieved 1May2013 from http://www.isprs.org/proceedings/2011/Gi4DM/PDF/PP12.pdf
13.
OlsonDWuD. Decision making with uncertainty and data mining. In: LiXWangSDongZ (eds), Advanced Data Mining and Applications (Lecture Notes in Artificial Intelligence, vol. 3584). Berlin: FRG: Springer, 2005, pp. 1–9.
14.
AgrawalVKohliVGuptaS. Computer aided robot selection: The multiple attribute decision making approach. Int J Product Res1991; 29(8): 1629–1644.
15.
ParkanCWuM. Decision-making and performance measurement models with applications to robot selection. Comput Ind Eng1999; 36(3): 503–523.
16.
ParkanCWuM. On the equivalence of operational performance measurement and multiple attribute decision making. Int J Product Res1997; 35(11): 2963–2988.
17.
KahramanCEnginOKubakOKayaL. Information systems outsourcing decisions using group decision-making approach. Eng Appl Artif Intell2009; 22(6): 832–841.
18.
ShyuraHShihH. A hybrid MCDM model for strategic vendor selection. Math Comput Model2006; 44(7–8): 749–761.
19.
FengCWangR. Considering the financial ratio on the performance evaluation of highway bus industry. Transport Rev2001; 21(4): 449–467.
20.
FoxWEvertonS. Using mathematical models in decision making methodologies to find key nodes in the Noordin dark network. Am J Operat Res2014. DOI: 10.4236/ajor.2014.
21.
SaatyRW. The analytic hierarchy process—what it is and how it is used. Math Modell1987; 9(3–5): 161–176. DOI: 10.1016/0270-0255(87)90473-8.
22.
SaatyTL. Decision making with the Analytic Hierarchy Process. Int J Services Sci2008; 1(1): 83–98.
23.
SaatyT. The Analytic Hierarchy Process. New York, NY: McGraw-Hill Book Company, 1980.
24.
HwangCLYoonK. Multiple attribute decision making: Methods and applications. New York, NY: Springer-Verlag, 1981.
25.
HwangCLaiYLiuT. A new approach for multiple objective decision making. Comput Operat Res1993; 20: 889–899.
26.
LeonelliR. Enhancing a decision support tool with sensitivity analysis. Thesis, University of Manchester, 2012.
27.
BakerTZabinskyZ. A multicriteria decision making model for reverse logistics using Analytical Hierarchy Process. Omega2011; 39: 558–573.
28.
HurlyWJ. The Analytical Hierarchy Process: A note on an approach to sensitivity which preserves rank order. Comput Operat Res2001; 28: 185–188.
29.
ButlerJJiaJDyerJ. Simulation techniques for the sensitivity analysis of multi-criteria decision models. Eur J Operat Res1997; 103: 531–546.
30.
AlinezhadAAminiA. Sensitivity Analysis of TOPSIS technique: The results of change in the weight of one attribute on the final ranking of alternatives. J Opt Indust Eng2011; 7: 23–28.
31.
TriantaphyliouELinC. Development and evaluation of five fuzzy multi-attribute decision making methods. Int J Approx Reason1996; 14(4): 281–310.
TriantaphyllouEMannSH. Using the analytic hierarchy process for decision making in engineering applications: some challenges. Int J Indust Eng1995; 2(1): 35–44.
ThackerBHDoeblingSWHemezFMAndersonMCPepinJERodriguezEA. Concepts of Model Verification and Validation. Los Alamos, NM: Los Alamos National Laboratory, 2004.
