Research article

Closest reference point on the strong efficient frontier in data envelopment analysis

  • Received: 03 September 2019 Accepted: 19 December 2019 Published: 27 December 2019
  • MSC : 90B30

  • Data envelopment analysis (DEA) is a data-oriented procedure to evaluate the relative performances of a set of homogenous decision making units (DMUs) with multiple incommensurate inputs and outputs. Performance measurement using tools such as DEA needs to construct an empirical production technology set. In this analysis, DMUs are partitioned into two groups: efficient and inefficient. Inefficient DMUs are projected onto efficient frontier in such a way that their inputs are reduced and their outputs are increased. In this sense, finding a projection point with the shortest distance is important and it is a most frequently studied subject in the field of DEA. In this paper, a two-steps procedure is proposed to determine a projection point on the efficient frontier with closest distance. The reference point is constructed in such a way that it is located on the strong defining hyperplane of the DEA technology set. As we will show, the low computational efforts and the guarantee of determining an efficient projection point on the strong efficient frontier are the two important advantages of the proposed model.To show the applicability of the proposed approach, a real case on 28 international airlines is given.

    Citation: Akbar Moradi, Alireza Amirteimoori, Sohrab Kordrostami, Mohsen Vaez-Ghasemi. Closest reference point on the strong efficient frontier in data envelopment analysis[J]. AIMS Mathematics, 2020, 5(2): 811-827. doi: 10.3934/math.2020055

    Related Papers:

    [1] Samantha L Elliott, Emek Kose, Allison L Lewis, Anna E Steinfeld, Elizabeth A Zollinger . Modeling the stem cell hypothesis: Investigating the effects of cancer stem cells and TGF−β on tumor growth. Mathematical Biosciences and Engineering, 2019, 16(6): 7177-7194. doi: 10.3934/mbe.2019360
    [2] Urszula Ledzewicz, Behrooz Amini, Heinz Schättler . Dynamics and control of a mathematical model for metronomic chemotherapy. Mathematical Biosciences and Engineering, 2015, 12(6): 1257-1275. doi: 10.3934/mbe.2015.12.1257
    [3] H. J. Alsakaji, F. A. Rihan, K. Udhayakumar, F. El Ktaibi . Stochastic tumor-immune interaction model with external treatments and time delays: An optimal control problem. Mathematical Biosciences and Engineering, 2023, 20(11): 19270-19299. doi: 10.3934/mbe.2023852
    [4] Craig Collins, K. Renee Fister, Bethany Key, Mary Williams . Blasting neuroblastoma using optimal control of chemotherapy. Mathematical Biosciences and Engineering, 2009, 6(3): 451-467. doi: 10.3934/mbe.2009.6.451
    [5] Peter Hinow, Philip Gerlee, Lisa J. McCawley, Vito Quaranta, Madalina Ciobanu, Shizhen Wang, Jason M. Graham, Bruce P. Ayati, Jonathan Claridge, Kristin R. Swanson, Mary Loveless, Alexander R. A. Anderson . A spatial model of tumor-host interaction: Application of chemotherapy. Mathematical Biosciences and Engineering, 2009, 6(3): 521-546. doi: 10.3934/mbe.2009.6.521
    [6] Xin Chen, Tengda Li, Will Cao . Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction. Mathematical Biosciences and Engineering, 2023, 20(10): 17589-17607. doi: 10.3934/mbe.2023781
    [7] Qingfeng Tang, Guohong Zhang . Stability and Hopf bifurcations in a competitive tumour-immune system with intrinsic recruitment delay and chemotherapy. Mathematical Biosciences and Engineering, 2021, 18(3): 1941-1965. doi: 10.3934/mbe.2021101
    [8] Hsiu-Chuan Wei . Mathematical modeling of tumor growth: the MCF-7 breast cancer cell line. Mathematical Biosciences and Engineering, 2019, 16(6): 6512-6535. doi: 10.3934/mbe.2019325
    [9] Donggu Lee, Sunju Oh, Sean Lawler, Yangjin Kim . Bistable dynamics of TAN-NK cells in tumor growth and control of radiotherapy-induced neutropenia in lung cancer treatment. Mathematical Biosciences and Engineering, 2025, 22(4): 744-809. doi: 10.3934/mbe.2025028
    [10] G. V. R. K. Vithanage, Hsiu-Chuan Wei, Sophia R-J Jang . Bistability in a model of tumor-immune system interactions with an oncolytic viral therapy. Mathematical Biosciences and Engineering, 2022, 19(2): 1559-1587. doi: 10.3934/mbe.2022072
  • Data envelopment analysis (DEA) is a data-oriented procedure to evaluate the relative performances of a set of homogenous decision making units (DMUs) with multiple incommensurate inputs and outputs. Performance measurement using tools such as DEA needs to construct an empirical production technology set. In this analysis, DMUs are partitioned into two groups: efficient and inefficient. Inefficient DMUs are projected onto efficient frontier in such a way that their inputs are reduced and their outputs are increased. In this sense, finding a projection point with the shortest distance is important and it is a most frequently studied subject in the field of DEA. In this paper, a two-steps procedure is proposed to determine a projection point on the efficient frontier with closest distance. The reference point is constructed in such a way that it is located on the strong defining hyperplane of the DEA technology set. As we will show, the low computational efforts and the guarantee of determining an efficient projection point on the strong efficient frontier are the two important advantages of the proposed model.To show the applicability of the proposed approach, a real case on 28 international airlines is given.




    [1] R. D. Banker, A. Charnes, W. W. Cooper, Some models for estimating technical and scale inefficiency in data envelopment analysis, Manage. Sci., 30 (1984), 1078-1092. doi: 10.1287/mnsc.30.9.1078
    [2] A. Emrouznejad, R. Banker, L. Neralić, Advances in data envelopment analysis: Celebrating the 40th anniversary of DEA and the 100th anniversary of Professor Abraham Charnes' birthday, Eur. J. Oper. Res., 278 (2019), 365-367. doi: 10.1016/j.ejor.2019.02.020
    [3] A. Emrouznejad, B. R. Parker, G. Tavares, Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Econ. Plan. Sci., 42 (2008), 151-157. doi: 10.1016/j.seps.2007.07.002
    [4] W. Briec, B. Lemaire, Technical efficiency and distance to a reverse convex set, Eur. J. Oper. Res., 114 (1999), 178-187. doi: 10.1016/S0377-2217(98)00089-7
    [5] F. X. Frei, P. T. Harker, Projections onto efficient frontiers: Theoretical and computational extensions to DEA, J. Prod. Anal., 11 (1999), 275-300. doi: 10.1023/A:1007746205433
    [6] E. Gonzaxlez, A. Axlvarez, From efficiency measurement to efficiency improvement: The choice of a relevant benchmark, Eur. J. Oper. Res., 133 (2001), 512-520. doi: 10.1016/S0377-2217(00)00195-8
    [7] S. Lozano, G. Villa, Determining a sequence of targets in DEA, J. Oper. Res. Soc., 56 (2005), 1439-1447. doi: 10.1057/palgrave.jors.2601964
    [8] A. Amirteimoori, S. Kordrostami, A Euclidean distance-based measure of efficiency in data envelopment analysis, Optimization, 59 (2010), 985-996. doi: 10.1080/02331930902878333
    [9] J. Aparicio, J. T. Pastor, A well-defined efficiency measure for dealing with closest targets in DEA, Appl. Math. Comput., 219 (2013), 9142-9154.
    [10] J. Aparicio, J. T. Pastor, On how to properly calculate the Euclidean distance-based measure in DEA, Optimization, 63 (2014), 421-432. doi: 10.1080/02331934.2012.655692
    [11] J. Aparicio, J. L. Ruiz, I. Sirvent, Closest targets and minimum distance to the Pareto-efficient frontier in DEA, J. Prod. Anal., 28 (2007), 209-218. doi: 10.1007/s11123-007-0039-5
    [12] J. Aparicio, J. T. Pastor, Closest targets and strong monotonicity on the strongly efficient frontier in DEA, Omega, 44 (2014), 51-57. doi: 10.1016/j.omega.2013.10.001
    [13] Q. An, Z. Pang, H. Cen, et al. Closest targets in environmental efficiency evaluation based on enhanced Russell measure, Ecol. Indic., 51 (2015), 59-66. doi: 10.1016/j.ecolind.2014.09.008
    [14] R. R. Russell, Measures of technical efficiencies, J. Econ. Theor., 35 (1985), 109-126. doi: 10.1016/0022-0531(85)90064-X
    [15] J. Aparicio, J. M. Cordero, J. T. Pastor, The determination of the least distance to the strongly efficient frontier in data envelopment analysis oriented models: modelling and computational aspects, Omega, 71 (2017), 1-10. doi: 10.1016/j.omega.2016.09.008
    [16] S. Razipour-GhalehJough, F. H. Lotfi, G. Jahanshahloo, et al. Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis, Ann. Oper. Res., 27 (2019), 1-33.
    [17] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res., 2 (1978), 429-444. doi: 10.1016/0377-2217(78)90138-8
    [18] K. Tone, A slack-based measure of efficiency in DEA, Eur. J. Oper. Res., 130 (2001), 498-509. doi: 10.1016/S0377-2217(99)00407-5
    [19] S. C. Ray, Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research, Cambridge University Press, 2004.
  • This article has been cited by:

    1. Heinz Schättler, Urszula Ledzewicz, 2015, Chapter 8, 978-1-4939-2971-9, 317, 10.1007/978-1-4939-2972-6_8
    2. U. Ledzewicz, H. Schättler, S. Anita, N. Hritonenko, G. Marinoschi, A. Swierniak, A Review of Optimal Chemotherapy Protocols: From MTD towards Metronomic Therapy, 2014, 9, 0973-5348, 131, 10.1051/mmnp/20149409
    3. Heinz Schättler, Urszula Ledzewicz, Behrooz Amini, Dynamical properties of a minimally parameterized mathematical model for metronomic chemotherapy, 2016, 72, 0303-6812, 1255, 10.1007/s00285-015-0907-y
    4. Heinz Schättler, Urszula Ledzewicz, 2015, Chapter 1, 978-1-4939-2971-9, 1, 10.1007/978-1-4939-2972-6_1
    5. Gary An, Swati Kulkarni, An agent-based modeling framework linking inflammation and cancer using evolutionary principles: Description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data, 2015, 260, 00255564, 16, 10.1016/j.mbs.2014.07.009
    6. Urszula Ledzewicz, Behrooz Amini, Heinz Schättler, Dynamics and control of a mathematical model for metronomic chemotherapy, 2015, 12, 1551-0018, 1257, 10.3934/mbe.2015.12.1257
    7. Nicolas Houy, François Le Grand, Francesco Pappalardo, Optimal dynamic regimens with artificial intelligence: The case of temozolomide, 2018, 13, 1932-6203, e0199076, 10.1371/journal.pone.0199076
    8. Dominique Barbolosi, Joseph Ciccolini, Bruno Lacarelle, Fabrice Barlési, Nicolas André, Computational oncology — mathematical modelling of drug regimens for precision medicine, 2016, 13, 1759-4774, 242, 10.1038/nrclinonc.2015.204
    9. Urszula Ledzewicz, Heinz Schättler, 2014, Chapter 7, 978-1-4939-1792-1, 157, 10.1007/978-1-4939-1793-8_7
    10. Urszula Ledzewicz, Heinz Schättler, On the Role of the Objective in the Optimization of Compartmental Models for Biomedical Therapies, 2020, 187, 0022-3239, 305, 10.1007/s10957-020-01754-2
    11. Raimund Bürger, Gerardo Chowell, Leidy Yissedt Lara-Díaz, Measuring differences between phenomenological growth models applied to epidemiology, 2021, 334, 00255564, 108558, 10.1016/j.mbs.2021.108558
    12. Malgorzata Kardynska, Daria Kogut, Marcin Pacholczyk, Jaroslaw Smieja, Mathematical modeling of regulatory networks of intracellular processes – Aims and selected methods, 2023, 21, 20010370, 1523, 10.1016/j.csbj.2023.02.006
    13. Fuat Gurcan, Senol Kartal, Chaos and its control in a discretized fractional order tumor-immune system Model with benign and malignant case, 2025, 1598-5865, 10.1007/s12190-025-02491-3
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4649) PDF downloads(460) Cited by(1)

Article outline

Figures and Tables

Figures(1)  /  Tables(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog