K-best Night Vision Devices by Multi-Criteria Mixed-Integer Optimization Modeling
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.
[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
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1877071943, 2005.
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intelligent night vision system for automobiles. In: Conference on
Machine Vision Applications, May 20-22, Yokohama, Japan, 2009
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and TOPSIS methods under fuzzy environment”, Expert Systems with
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decision aid approach for the selection of the best compromise
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[9] W.-W. Wu, Y.-T. Lee. “Selecting knowledge management strategies by
using the analytic network process”, Expert Systems with Applications,
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[10] X. Wang, E. Triantaphyllou. “Ranking irregularities when evaluating
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“PROMETHEE: A comprehensive literature review on methodologies
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[15] T. L. Saaty. Making and validating complex decisions with the
AHP/ANP. Journal of Systems Science and Systems Engineering, vol.
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[16] D. Borissova, I. Mustakerov. “A working distance formula for night
vision devices quality preliminary information”, Cybernetics and
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[17] D-F Li. Relative ratio method for multiple attribute decision making
problems, International Journal of Information Technology & Decision
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[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
[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
@article{"International Journal of Information, Control and Computer Sciences:65305", author = "Daniela I. Borissova and Ivan C. Mustakerov", title = "K-best Night Vision Devices by Multi-Criteria Mixed-Integer Optimization Modeling ", 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. ", volume = "7", number = "10", pages = "1271-6", }
