K-best Night Vision Devices by Multi-Criteria Mixed-Integer Optimization Modeling
Authors: Daniela I. Borissova, Ivan C. Mustakerov
Abstract:
The paper describes an approach for defining of k-best night vision devices based on multi-criteria mixed-integer optimization modeling. The parameters of night vision devices are considered as criteria that have to be optimized. Using different user preferences for the relative importance between parameters different choice of k-best devices can be defined. An ideal device with all of its parameters at their optimum is used to determine how far the particular device from the ideal one is. A procedure for evaluation of deviation between ideal solution and k-best solutions is presented. The applicability of the proposed approach is numerically illustrated using real night vision devices data. The proposed approach contributes to quality of decisions about choice of night vision devices by making the decision making process more certain, rational and efficient.
Keywords: K-best devices, mixed-integer model, multi-criteria problem, night vision devices.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088206
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[1] J. G. Winkel, L. Faber. Civil use of night vision goggles within the National Airspace System, In: Proc. SPIE, no 4361, pp. 159-163, 2001
[2] N.S. Martinelli, R. Seoane. Automotive night vision system. In: Proc. SPIE Thermosense XXI; Dennis H. LeMieux, John R. Snell, Jr.; Eds., vol. 3700, pp. 343-346, 1999.
[3] Aviation Research Report B2004/0152. Night vision goggles in civil helicopter operations. Australian Transport Safety Bureau, ISBN 1877071943, 2005.
[4] Y. Tsz-Ho, Moon,Y-S., J. Chen, H-K. Fung, H-F. Ko, R. Wang. An intelligent night vision system for automobiles. In: Conference on Machine Vision Applications, May 20-22, Yokohama, Japan, 2009
[5] M. Dagdeviren, S. Yavuz, N. Kilinc. “Weapon selection using the AHP and TOPSIS methods under fuzzy environment”, Expert Systems with Applications, vol. 36, pp. 8143-8151, 2009.
[6] I. Mergias, K. Moustakas, A. Papadopoulos, M. Loizidou, “Multicriteria decision aid approach for the selection of the best compromise management scheme for ELVs: The case of Cyprus”, Journal of Hazardous Materials, vol. 147, pp. 706-717, 2007.
[7] C. A. Nelson, “A scoring model for flexible manufacturing system project selection”, European Journal of Operational Research, vol. 24, pp. 346-359, 1986.
[8] M. S. Garcia-Cascales, M. T. Lamata. “Selection of a cleaning system for engine maintenance based on the analytic hierarchy process”, Computers & Industrial Engineering, vol. 56, pp. 1442-1451, 2009.
[9] W.-W. Wu, Y.-T. Lee. “Selecting knowledge management strategies by using the analytic network process”, Expert Systems with Applications, vol. 32, pp. 841-847, 2007.
[10] X. Wang, E. Triantaphyllou. “Ranking irregularities when evaluating alternatives by using some ELECTRE methods”, Omega, vol. 36, pp. 45-63, 2008.
[11] M. Behzadian, R. B. Kazemzadeh, A. Albadvi, M. Aghdasi. “PROMETHEE: A comprehensive literature review on methodologies and applications”, European Journal of Operational Research, vol. 200, pp. 198-215, 2010.
[12] N. Kalouptsidis, K. Koutroumbas, V. Psaraki. “Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule”, European Journal of Operational Research, vol. 176, pp. 1778-1794, 2007.
[13] M. Behzadian, S. K. Otaghsara, M. Yazdani, J. Ignatius. “A state-of theart survey of TOPSIS applications”, Expert Systems with Applications, vol. 39, pp. 13051-13069, 2012.
[14] O. Kulak, S. Cebi, C. Kahraman. “Applications of axiomatic design principles: A literature review”, Expert Systems with Applications, vol. 37, pp. 6705-6717, 2010.
[15] T. L. Saaty. Making and validating complex decisions with the AHP/ANP. Journal of Systems Science and Systems Engineering, vol. 14, pp. 1-36, 2005.
[16] D. Borissova, I. Mustakerov. “A working distance formula for night vision devices quality preliminary information”, Cybernetics and Information Technologies, vol. 6, pp. 85-92, 2006.
[17] D-F Li. Relative ratio method for multiple attribute decision making problems, International Journal of Information Technology & Decision Making, vol.08, pp. 289-311, 2009.
[18] D-2MV Night Vision Goggle, http://www.nightoptics.com/includes/ tech_specs/TS_NO-D-2MV.pdf
[19] Rigel 3250 Compact Night Vision Goggles, http://www.opticsplanet.com/ri32conivigo.html
[20] ATN Night Cougar 2, http://www.atncorp.com/atn-nightcougar-2- archived-product
[21] ATN Night Cougar CGT, http://www.atncorp.com/atn-nightcougar-cgtarchived- product
[22] ATN Night Cougar 3, http://www.atncorp.com/atn-nightcougar-3- archived-product
[23] ATN Night Cougar 4, http://www.atncorp.com/atn-nightcougar-4- archived-product
[24] ATN PS23-2, http://www.atncorp.com/atn-ps23-2-archived-product
[25] ATN PS23-CGT, http://www.atncorp.com/atn-ps23-cgt-archivedproduct
[26] ATN PS23-3, http://www.atncorp.com/atn-ps23-3-archived-product
[27] ATN PS23-4, http://www.atncorp.com/atn-ps23-4-archived-product
[28] T. Marler. A study of multi-objective optimization methods for engineering applications, PhD Thesis, pages 351, 2005.
[29] A.Osyczka. Multicriterion optimization in engineering with fortran programs, John Wilev and sons, New York, 1984.
[30] Kim, I.Y., O.L. de Weck, “Adaptive weighted-sum method for biobjective optimization: Pareto front generation”, Structural and multidisciplinary optimization, vol. 29, No 2, pp. 149-158, 2005.
[31] Lindo Systems ver. 12, http://www.lindo.com