Quantum algorithms for solving data envelopment analysis models

Malekmohammadi, Najmeh; Ebrahimnejad, Ali; Sayedsalehi, Samira

doi:10.22075/ijnaa.2022.26054.3208

اداره چاپ و انتشارات دانشگاه سمنان

تعداد نشریات	21
تعداد شماره‌ها	643
تعداد مقالات	9,418
تعداد مشاهده مقاله	68,155,534
تعداد دریافت فایل اصل مقاله	42,291,456

	Quantum algorithms for solving data envelopment analysis models
International Journal of Nonlinear Analysis and Applications
مقاله 238، دوره 14، شماره 1، فروردین 2023، صفحه 3063-3070 اصل مقاله (426.77 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.26054.3208
نویسندگان
Najmeh Malekmohammadi^* ¹؛ Ali Ebrahimnejad²؛ Samira Sayedsalehi³
¹Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran.
²Department of Mathematics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
³Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
تاریخ دریافت: 06 بهمن 1400، تاریخ بازنگری: 14 اردیبهشت 1401، تاریخ پذیرش: 22 اردیبهشت 1401
چکیده
In traditional optimization models, classical computers have been used to solve mathematical linear or non-linear models. In addition, by data envelopment analysis (DEA) models one can evaluate the relative efficiency of the decision-making units originally in the form of mathematical non-linear models. In this paper, a new attitude is presented in which quantum computers and quantum algorithms can be used to solve DEA models by two different methods. This suggested attitude is worthy to mention as this subject is new and will attract researchers to discuss and improve it.
کلیدواژه‌ها
Data envelopment analysis؛ Non-linear Models؛ Quantum Algorithm؛ Quantum Optimization

مراجع
[1] M.S. Bazaraa, J.J. Jarvis and H.D. Sherali, Linear programming and network flows, John Wiley and Sons, 2010. [2] A. Charnes, W.W. Cooper and E.L. Rhodes, Measuring the efficiency of decision making units, Euro. J. Oper. Res. 2 (1978), 429–444. [3] K. Chen, Matrix Preconditioning Techniques and Applications, Cambridge University Press, 2005. [4] W.W. Cooper, LM. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver Software, Kluwer Academic Publishers: Norwell, MA, 2000. [5] A.W. Harrow and A. Hassidim, S. Lloyd, Quantum algorithm for linear systems of equation, Phys. Rev. Lett. 103 (2009), 150–502. [6] T. Hogg and D. Portnov, Quantum optimization, Inf. Sci. 128 (2000), 181–197. [7] D. McMahon, Quantum computing explained, John Wiley and Sons, 2008. [8] M. Nielsen and I. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, New York, 2010. [9] M. Side and V. Erol, Applying quantum optimization algorithms for linear programming, doi:10.20944/preprints201703.0238.v2, April 2017. [10] D.A. Spielman and S.H. Teng , Nearly-linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems, SIAM J. Matrix Anal. Appl. 35 (2014), no. 3, 835–885. [11] J. Sun, J. Liu and W. Xu, Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems, Int J. Comput. Math. 84 (2007), 261–272. [12] A.D. Athanassopoulos, N. Lambroukos and L.M. Seiford, Data envelopment scenario analysis for setting targets to electricity generating plants, Euro. J. Operational Res. 115 (1999), 413–428. [13] AJ. Manuel, S. Carmen and S. Lozano, A fuzzy DEA slacks-based approach, J. Comput. Applied Math. 404 (2022), 113–180. [14] F. Hosseinzadeh Lotfi, A. Ebrahimnejad, M. Vaez-Ghasemi and Z. Moghaddas, Data Envelopment Analysis with R, Springer, 2020. [15] R. Banker, Stochastic data envelopment analysis, Data Envelop Analy. J. 5 (2021), 281–309.
آمار تعداد مشاهده مقاله: 17,205 تعداد دریافت فایل اصل مقاله: 7,850

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب

پیوندهای مفید

پیوندهای مفید

آمار

Quantum algorithms for solving data envelopment analysis models