Using Multi-Attribute Decision Methods in Mathematical Modeling to Produce an Order of Merit List of High Valued Terrorists
Author(s)
William P. Fox
ABSTRACT
The authors present a methodology and an example of preparing an order of merit list to rank terrorist based upon decision maker weights. This research used an old terrorist data set as our base data to keep the information unclassified. This data is used to demonstrate this methodology. The authors perform numerical iterative criteria weight sensitivity analysis to show the effects on the model’s outputs in changes in the weights. Through their analysis the most critical criterion is identified.
The authors present a methodology and an example of preparing an order of merit list to rank terrorist based upon decision maker weights. This research used an old terrorist data set as our base data to keep the information unclassified. This data is used to demonstrate this methodology. The authors perform numerical iterative criteria weight sensitivity analysis to show the effects on the model’s outputs in changes in the weights. Through their analysis the most critical criterion is identified.
KEYWORDS
Ranking Terrorists, TOPSIS, AHP, Criteria Weights, Pairwise Comparisons, Sensitivity Analysis
Ranking Terrorists, TOPSIS, AHP, Criteria Weights, Pairwise Comparisons, Sensitivity Analysis
Cite this paper
Fox, W. (2014) Using Multi-Attribute Decision Methods in Mathematical Modeling to Produce an Order of Merit List of High Valued Terrorists. American Journal of Operations Research, 4, 365-374. doi: 10.4236/ajor.2014.46035.
Fox, W. (2014) Using Multi-Attribute Decision Methods in Mathematical Modeling to Produce an Order of Merit List of High Valued Terrorists. American Journal of Operations Research, 4, 365-374. doi: 10.4236/ajor.2014.46035.
References
[1] National Strategy for Combating Terror (NSCT) (2006) Washington DC.
http://www.cbsnews.com/htdocs/pdf/NSCT0906.pdf,accessed [2] Field Manual, FM 34-8-2 (1998) Intelligence Officers Handbook, HQ Department of the Army, Washington DC. [3] Ormond, B. and Williams, A. (2014) Using Multi-Attribute Decision Making and Comparative Evidence to Improve Counter-Terrorist Decisions. Master’s Thesis, NPS, June 2014. [4] Fox, W. and Everton, S. (2014) Mathematical Modeling in Social Network Analysis: Using Data Envelopment Analysis and Analytical Hierarchy Process to Find Node Influences in a Social Network. Journal of Defense Modeling and Simulation 2014 publication, 2014, 1-9. (published on line, journal due in summer 2014) [5] Fox, W. and Everton, S.F. (2014) Using Mathematical Models in Decision Making Methodologies to Find Key Nodes in the Noordin Dark Network. American Journal of Operations Research, 1-13 (on-line). [6] Fox, W. and Thompson, M.N. (2014) Phase Targeting of Terrorist Attacks: Simplifying Complexity with Analytical Hierarchy Process. International Journal of Decision Sciences, 5, 58-64. [7] Thompson, N. and Fox, W. (2014) Phase Targeting of Terrorist Attacks: Simplifying Complexity with TOPSIS. Journal of Defense Management, 4, 1.
http://dx.doi.org/10.4172/2167-0374.1000116 [8] Twedell, P. and Edmonds, W. (2008) Countermetrics System (CMS): Using Data and Comparative Evidence to Improve Human-Targeting Decisions. Master’s Thesis, Naval Postgraduate School. [9] Hwang, C.L. and Yoon, K. (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
http://dx.doi.org/10.1007/978-3-642-48318-9 [10] Malczewski, J. (1996) GIS-Based Approach to Multiple Criteria Group Decision-Making. International Journal of Geographical Information Science - GIS, 10, 955-971.
http://dx.doi.org/10.1080/02693799608902119 [11] Napier, J. (1992) Industrial Base Program Item Selection Indicators Analytical Enhancements. Department of Defense Pamphlet, DLA-93-P20047. [12] Wang, Y. and He, Z. (2008) Improved TOPSIS Methods for Multi-Response Optimization. IEEE.
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04721521 [13] Ozturk, D. and Batuk, F. (2011) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for Spatial Decision Problem. Proceedings ISPRS.
http://www.isprs.org/proceedings/2011/Gi4DM/PDF/PP12.pdf [14] Olson, D. and Wu, D. (2005) Decision Making with Uncertainty and Data Mining. In: Li, X., Wang, S. and Dong, Z., Eds., Lecture Notes in Artificial Intelligence, Springer, Berlin, 1-9. [15] Agrawal, V., Kohli, V. and Gupta, S. (1991) Computer Aided Robot Selection: The Multiple Attribute Decision Making Approach. International Journal of Production Research, 29, 1629-1644.
http://dx.doi.org/10.1080/00207549108948036 [16] Parkan, C. and Wu, M.L. (1999) Decision-Making and Performance Measurement Models with Applications to Robot Selection. Computers & Industrial Engineering, 36, 503-523.
http://dx.doi.org/10.1016/S0360-8352(99)00146-1 [17] Parkan, C. and Wu, M.L. (1997) On the Equivalence of Operational Performance Measurement and Multiple Attribute Decision Making. International Journal of Production Research, 35, 2963-2988.
http://dx.doi.org/10.1080/002075497194246 [18] Kahraman, C., Engin, O., Kubak, O. and Kaya, L. (2009) Information Systems Outsourcing Decisions Using a Group Decision-Making Approach. Engineering Applications of Artificial Intelligence, 22, 832-841.
http://dx.doi.org/10.1016/j.engappai.2008.10.009 [19] Shyura, H. and Shih, H. (2006) A Hybrid MCDM Model for Strategic Vendor Selection. Mathematical and Computer Modelling, 44, 749-761.
http://dx.doi.org/10.1016/j.mcm.2005.04.018 [20] Feng, C.M. and Wang, R.T. (2001) Considering the Financial Ratios on the Performance Evaluation of Highway Bus Industry. Transport Reviews, 21, 449-467.
http://dx.doi.org/10.1080/01441640010020304 [21] Leonelli, R. (2012) Enhancing a Decision Support Tool with Sensitivity Analysis. Master’s Thesis, University of Manchester, Manchester. [22] Chen, H.Y. and Kocaoglu, D. (2008) A Sensitivity Analysis Algorithm for Hierarchical Decision Models. European Journal of Operational Research, 185, 266-288.
http://dx.doi.org/10.1016/j.ejor.2006.12.029 [23] Baker, T. and Zabinsky, Z. (2011) A Multicriteria Decision Making Model for Reverse Logistics Using Analytical Hierarchy Process. Omega, 39, 558-573.
http://dx.doi.org/10.1016/j.omega.2010.12.002 [24] Hurly, W.J. (2001) The Analytical Hierarchy Process: A Note on an Approach to Sensitivity Which Preserves Rank Order. Computers and Operations Research, 28, 185-188.
http://dx.doi.org/10.1016/S0305-0548(99)00125-2 [25] Butler, J., Jia, J. and Dyer, J. (1997) Simulation Techniques for the Sensitivity Analysis of Multi-Criteria Decision Models. European Journal of Operational Research, 103, 531-546.
http://dx.doi.org/10.1016/S0377-2217(96)00307-4 [26] Alinzhad, A. and Amini, A. (2011) Sensitivity Analysis of TOPSIS Technique: The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives. Journal of Optimization in Industrial Engineering, 7, 23-28.
[1] National Strategy for Combating Terror (NSCT) (2006) Washington DC.
http://www.cbsnews.com/htdocs/pdf/NSCT0906.pdf,accessed [2] Field Manual, FM 34-8-2 (1998) Intelligence Officers Handbook, HQ Department of the Army, Washington DC. [3] Ormond, B. and Williams, A. (2014) Using Multi-Attribute Decision Making and Comparative Evidence to Improve Counter-Terrorist Decisions. Master’s Thesis, NPS, June 2014. [4] Fox, W. and Everton, S. (2014) Mathematical Modeling in Social Network Analysis: Using Data Envelopment Analysis and Analytical Hierarchy Process to Find Node Influences in a Social Network. Journal of Defense Modeling and Simulation 2014 publication, 2014, 1-9. (published on line, journal due in summer 2014) [5] Fox, W. and Everton, S.F. (2014) Using Mathematical Models in Decision Making Methodologies to Find Key Nodes in the Noordin Dark Network. American Journal of Operations Research, 1-13 (on-line). [6] Fox, W. and Thompson, M.N. (2014) Phase Targeting of Terrorist Attacks: Simplifying Complexity with Analytical Hierarchy Process. International Journal of Decision Sciences, 5, 58-64. [7] Thompson, N. and Fox, W. (2014) Phase Targeting of Terrorist Attacks: Simplifying Complexity with TOPSIS. Journal of Defense Management, 4, 1.
http://dx.doi.org/10.4172/2167-0374.1000116 [8] Twedell, P. and Edmonds, W. (2008) Countermetrics System (CMS): Using Data and Comparative Evidence to Improve Human-Targeting Decisions. Master’s Thesis, Naval Postgraduate School. [9] Hwang, C.L. and Yoon, K. (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
http://dx.doi.org/10.1007/978-3-642-48318-9 [10] Malczewski, J. (1996) GIS-Based Approach to Multiple Criteria Group Decision-Making. International Journal of Geographical Information Science - GIS, 10, 955-971.
http://dx.doi.org/10.1080/02693799608902119 [11] Napier, J. (1992) Industrial Base Program Item Selection Indicators Analytical Enhancements. Department of Defense Pamphlet, DLA-93-P20047. [12] Wang, Y. and He, Z. (2008) Improved TOPSIS Methods for Multi-Response Optimization. IEEE.
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=04721521 [13] Ozturk, D. and Batuk, F. (2011) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for Spatial Decision Problem. Proceedings ISPRS.
http://www.isprs.org/proceedings/2011/Gi4DM/PDF/PP12.pdf [14] Olson, D. and Wu, D. (2005) Decision Making with Uncertainty and Data Mining. In: Li, X., Wang, S. and Dong, Z., Eds., Lecture Notes in Artificial Intelligence, Springer, Berlin, 1-9. [15] Agrawal, V., Kohli, V. and Gupta, S. (1991) Computer Aided Robot Selection: The Multiple Attribute Decision Making Approach. International Journal of Production Research, 29, 1629-1644.
http://dx.doi.org/10.1080/00207549108948036 [16] Parkan, C. and Wu, M.L. (1999) Decision-Making and Performance Measurement Models with Applications to Robot Selection. Computers & Industrial Engineering, 36, 503-523.
http://dx.doi.org/10.1016/S0360-8352(99)00146-1 [17] Parkan, C. and Wu, M.L. (1997) On the Equivalence of Operational Performance Measurement and Multiple Attribute Decision Making. International Journal of Production Research, 35, 2963-2988.
http://dx.doi.org/10.1080/002075497194246 [18] Kahraman, C., Engin, O., Kubak, O. and Kaya, L. (2009) Information Systems Outsourcing Decisions Using a Group Decision-Making Approach. Engineering Applications of Artificial Intelligence, 22, 832-841.
http://dx.doi.org/10.1016/j.engappai.2008.10.009 [19] Shyura, H. and Shih, H. (2006) A Hybrid MCDM Model for Strategic Vendor Selection. Mathematical and Computer Modelling, 44, 749-761.
http://dx.doi.org/10.1016/j.mcm.2005.04.018 [20] Feng, C.M. and Wang, R.T. (2001) Considering the Financial Ratios on the Performance Evaluation of Highway Bus Industry. Transport Reviews, 21, 449-467.
http://dx.doi.org/10.1080/01441640010020304 [21] Leonelli, R. (2012) Enhancing a Decision Support Tool with Sensitivity Analysis. Master’s Thesis, University of Manchester, Manchester. [22] Chen, H.Y. and Kocaoglu, D. (2008) A Sensitivity Analysis Algorithm for Hierarchical Decision Models. European Journal of Operational Research, 185, 266-288.
http://dx.doi.org/10.1016/j.ejor.2006.12.029 [23] Baker, T. and Zabinsky, Z. (2011) A Multicriteria Decision Making Model for Reverse Logistics Using Analytical Hierarchy Process. Omega, 39, 558-573.
http://dx.doi.org/10.1016/j.omega.2010.12.002 [24] Hurly, W.J. (2001) The Analytical Hierarchy Process: A Note on an Approach to Sensitivity Which Preserves Rank Order. Computers and Operations Research, 28, 185-188.
http://dx.doi.org/10.1016/S0305-0548(99)00125-2 [25] Butler, J., Jia, J. and Dyer, J. (1997) Simulation Techniques for the Sensitivity Analysis of Multi-Criteria Decision Models. European Journal of Operational Research, 103, 531-546.
http://dx.doi.org/10.1016/S0377-2217(96)00307-4 [26] Alinzhad, A. and Amini, A. (2011) Sensitivity Analysis of TOPSIS Technique: The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives. Journal of Optimization in Industrial Engineering, 7, 23-28.