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Properties of DEA-integrated balance and specialization measures. (English) Zbl 1311.90133

Summary: A recently proposed approach measures balance and specialization degrees as opposite key performance indicators in addition to the efficiency and effectiveness scores well known from data envelopment analysis (DEA). It has been integrated into the DEA methodology by formulating output-oriented models of CCR- and BCC-type and has successfully been applied to two case studies of a European pharmacy business as well as German business schools’ research performance. Because the models are of (non-linear) minimax-type the calculation of the scores is not straightforward. Therefore this paper analyses the properties of the models in order to better understand them and to improve the solution process. The properties derived allow to propose an efficient heuristic solution procedure. While some properties hold for more general models others are true only for models with special convex polyhedric cones defining the balance set. A main result states that specialization of a decision-making unit is essentially measured by its angle distance to the balance cone.

MSC:

90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
62-07 Data analysis (statistics) (MSC2010)
90C47 Minimax problems in mathematical programming
Full Text: DOI

References:

[1] Ahn H, Neumann L, Vazquez Novoa N (2012) Measuring the relative balance of DMUs. Eur J Oper Res 221:417-423 (corrigendum, 222:68) · Zbl 1253.90167 · doi:10.1016/j.ejor.2012.03.030
[2] Allen R, Athanassopoulos A, Dyson RG, Thanassoulis E (1997) Weights restrictions and value judgements in data envelopment analysis: evolution, development and future directions. Ann Oper Res 73:13-34 · Zbl 0890.90002 · doi:10.1023/A:1018968909638
[3] Allen R, Thanassoulis E (2004) Improving envelopment in data envelopment analysis. Eur J Oper Res 154:363-379 · Zbl 1146.90434 · doi:10.1016/S0377-2217(03)00175-9
[4] Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30:1078-1092 · Zbl 0552.90055 · doi:10.1287/mnsc.30.9.1078
[5] Charnes A, Cooper WW, Huang ZM, Sun DB (1990) Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. J Econometr 46:73-91 · Zbl 0712.90015 · doi:10.1016/0304-4076(90)90048-X
[6] Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429-444 · Zbl 0416.90080 · doi:10.1016/0377-2217(78)90138-8
[7] Dimitrov S, Sutton W (2010) Promoting symmetric weight selection in data envelopment analysis: a penalty function approach. Eur J Oper Res 200:281-288 · Zbl 1183.90227 · doi:10.1016/j.ejor.2008.11.043
[8] Dyckhoff H, Clermont M, Dirksen A, Mbock E (2013) Measuring balanced effectiveness and efficiency of German business schools’ research performance. Z Betriebswirt 3:39-60. (special issue, former version available at: http://ssrn.com/abstract=1990233) · Zbl 0416.90080
[9] Dyckhoff H, Mbock E, Gutgesell S (2014) Distance-based measures of specialization in multi criteria: A DEA-integrated method. J Multi-Crit Decis Anal. doi:10.1002/mcda.1532
[10] Dyson RG, Thanassoulis E (1988) Reducing weight flexibility in data envelopment analysis. J Oper Res Soc 39:563-576 · doi:10.1057/jors.1988.96
[11] Dyson RG, Allen R, Camanho AS, Podinovski VV, Sarrico CS, Shale EA (2001) Pitfalls and protocols in DEA. Eur J Oper Res 132:245-259 · Zbl 0980.90038 · doi:10.1016/S0377-2217(00)00149-1
[12] Färe R, Grosskopf S (2005) New directions: efficiency and productivity. Springer, New York
[13] Mbock E (2014) Balance measurement methods in data envelopment analysis. Unpublished Ph.D. thesis, RWTH Aachen University · Zbl 0994.90080
[14] Neely A, Gregory M, Platts K (1995) Performance measurement system design: a literature review and research agenda. Int J Oper Prod Manage 15:80-116 · doi:10.1108/01443579510083622
[15] Podinovski VV (2001) DEA models for the explicit maximisation of relative efficiency. Eur J Oper Res 131:572-586 · Zbl 0994.90080 · doi:10.1016/S0377-2217(00)00099-0
[16] Thanassoulis, E.; Portela, MCS; Despic, O.; Ried, HO (ed.); Lovell, CAK (ed.); Schmidt, SS (ed.), Data envelopment analysis: the mathematical programming approach to efficiency analysis, 251-420 (2008), New York · doi:10.1093/acprof:oso/9780195183528.003.0003
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