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2021 | 31 | nr 1 | 77--96
Tytuł artykułu

Detecting Congestion in DEA by Solving One Model

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The presence of input congestion is one of the key issues that result in lower efficiency and performance in decision-making units (DMUs). So, determination of congestion is of prime importance, and removing it improves the performance of DMUs. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Since the output of inefficient units can be increased by keeping the input constant through projecting on the weak efficiency frontier, it is unnecessary to determine the congested inefficient DMUs. Therefore, in this case, we solely determine congested vertex units. Towards this aim, only one LP model in DEA is proposed and the status of congestion (strong congestion and weak congestion) obtained. In our method, a vertex unit under evaluation is eliminated from the production technology, and then, if there exists an activity that belongs to the production technology with lower inputs and higher outputs compared with the omitted unit, we say vertex unit evidences congestion. One of the features of our model is that by solving only one LP model and with easier and fewer calculations compared to other methods, congested units can be identified. Data set obtained from Japanese chain stores for a period of 27 years is used to demonstrate the applicability of the proposed model and the results are compared with some previous methods. (original abstract)
Rocznik
Tom
31
Numer
Strony
77--96
Opis fizyczny
Twórcy
  • North Tehran Branch, Islamic Azad University, Tehran, Iran
autor
  • North Tehran Branch, Islamic Azad University, Tehran, Iran
  • Sohar University, Sohar, Oman
  • Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
  • North Tehran Branch, Islamic Azad University, Tehran, Iran
Bibliografia
  • [1] CHARNES A., COOPER W.W., RHODES E., Measuring the efficiency of decision-making units, Eur. J. Oper. Res., 1978, 2, 429-444.
  • [2] BANKER R.D., CHARNES A., COOPER W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manage. Sci., 1984, 30 (9), 1078-1092.
  • [3] COOPER W.W., SEIFORD L., TONE K., Introduction to data envelopment analysis and its uses, with DEA-Solver software and references, Springer, New York 2005.
  • [4] FӒRE R., SVENSSON L., Congestion of production factors, Econ. J. Econ. Soc., 1980, 48, 1745-1753.
  • [5] FӒRE R., GROSSKOPF S., Measuring congestion in production, J. Econ., 1983, 43, 257-271.
  • [6] FӒRE R., GROSSKOPF S., LOVELL C.A.K., The measurement of efficiency of production, Kluwer, Boston 1985.
  • [7] COOPER W.W., THOMPSON R.G., THRALL R.M., Introduction extensions and new developments in DEA, Ann. Oper. Res., 1996, 66, 3-45.
  • [8] BROCKETT P.L., COOPER W.W., SHIN H.C., WANG Y., Inefficiency and congestion in Chinese production before and after the 1978 economic reforms, Socio-Econ. Plan. Sci., 1998, 32, 1-20.
  • [9] COOPER W.W., DENG H., HUANG Z.M., LI S.L., A one model approach to congestion in DEA, Socio-Econ. Plan. Sci., 2002, 36, 231-238.
  • [10] JAHANSHAHLOO G.R., KHODABAKHSHI M., Suitable combination of inputs for improving outputs in DEA with determining input congestion considering textile industry of China, Appl. Math. Comp., 2004, 151, 263-273.
  • [11] KHODABAKHSHI M., Chance constrained additive input relaxation model in stochastic data envelopment analysis, J. Inf. Syst. Sci., 2010, 6 (1), 99-112.
  • [12] WEI Q.L., YAN H., Congestion and returns to scale in data envelopment analysis, Eur. J. Oper. Res., 2004, 153, 641-660.
  • [13] TONE K., SAHOO B.K., Degree of scale economics and congestion, a unified DEA approach, Eur. J. Oper. Res., 2004, 153, 641-660.
  • [14] SUEYOSHI T., SEKITIANI K., DEA congestion and returns to scale under an occurrence of multiple optimal projections, Eur. J. Oper. Res., 2009, 194, 592-607.
  • [15] MEHDILOOZAD M., ZHU J., SAHOO B.K., Identification of congestion in data envelopment analysis under the occurrence of multiple projections. A reliable method capable of dealing with negative data, Eur. J. Oper. Res., 2018, 265, 644-654.
  • [16] SHADAB M., SAATI S., FARZIPOOR SAEN R., MOSTAFAEE A., Measuring congestion by anchor points in DEA, J. Sadhana, 2019, DOI:10.1007/s12046-020-1274-y.
  • [17] EBRAHIMZADE ADIMI M., ROSTAMY-MALKHALIFEH M., HOSSEINZADEH LOTFI F., MEHRJOO R., A new linear to find the congestion hyperplane in DEA, J. Math. Sci., 2019, 13, 43-52.
  • [18] KERSTENS K., VAN DE WOESTYNE I., The remarkable incidence of congestion in production. A review. Empirical illustration, and research agenda. Data Env. Anal. J., 2019, 4 (2), 109-147.
  • [19] MOSTAFAEE A., SOHRAIEE S., The role of hyperplanes for characterizing suspicious units in DEA, J. Ann. Oper. Res., 2018, 275 (2), 531-549.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171620418

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