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Centralized DEA models with the possibility of downsizing

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  • S Lozano

    (Escuela Superior de Ingenieros, Avda. de los Descubrimientos)

  • G Villa

    (Escuela Superior de Ingenieros, Avda. de los Descubrimientos)

Abstract

While traditional data envelopment analysis (DEA) models assess the relative efficiency of similar, independent decision making units (DMUs) centralized DEA models aim at reallocating inputs and outputs among the units setting new input and output targets for each one. This system point of view is appropriate when the DMUs belong to a common organization that allocates their inputs and appropriates their outputs. This intraorganizational perspective opens up the possibility that greater technical efficiency for the organization as a whole might be achieved by closing down some of the existing DMUs. In this paper, we present three centralized DEA models that take advantage of this possibility. Although these models involve some binary variables, we present efficient solution approaches based on Linear Programming. We also present some numerical results of the proposed models for a small problem from the literature.

Suggested Citation

  • S Lozano & G Villa, 2005. "Centralized DEA models with the possibility of downsizing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 357-364, April.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:4:d:10.1057_palgrave.jors.2601838
    DOI: 10.1057/palgrave.jors.2601838
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    References listed on IDEAS

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    2. Contreras, I. & Lozano, S., 2022. "Size efficiency, splits and merger gains, and centralized resource reallocation of Spanish public universities," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
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    6. Cesaroni, Giovanni, 2020. "Technically and cost-efficient centralized allocations in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    7. Barnum, Darold T. & Karlaftis, Matthew G. & Tandon, Sonali, 2011. "Improving the efficiency of metropolitan area transit by joint analysis of its multiple providers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1160-1176.
    8. S Lozano & G Villa, 2010. "DEA-based pre-merger planning tool," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1485-1497, October.
    9. Peyrache, Antonio, 2015. "Cost constrained industry inefficiency," European Journal of Operational Research, Elsevier, vol. 247(3), pages 996-1002.
    10. A Z Milioni & J V G de Avellar & T N Rabello & G M de Freitas, 2011. "Hyperbolic frontier model: a parametric DEA approach for the distribution of a total fixed output," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1029-1037, June.
    11. Yunfei An & Dequn Zhou & Qunwei Wang, 2022. "Carbon emission reduction potential and its influencing factors in China’s coal-fired power industry: a cost optimization and decomposition analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3619-3639, March.
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    13. Ane Elixabete Ripoll-Zarraga & Sebastián Lozano, 2020. "A centralised DEA approach to resource reallocation in Spanish airports," Annals of Operations Research, Springer, vol. 288(2), pages 701-732, May.
    14. Contreras, I. & Lozano, S., 2020. "Allocating additional resources to public universities. A DEA bargaining approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    15. Lozano, Sebastián & Contreras, Ignacio, 2022. "Centralised resource allocation using Lexicographic Goal Programming. Application to the Spanish public university system," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    16. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    17. Silva, Rodrigo Cesar & Milioni, Armando Zeferino, 2012. "The Adjusted Spherical Frontier Model with weight restrictions," European Journal of Operational Research, Elsevier, vol. 220(3), pages 729-735.
    18. Fang, Lei, 2013. "A generalized DEA model for centralized resource allocation," European Journal of Operational Research, Elsevier, vol. 228(2), pages 405-412.
    19. Wang Hong Li & Liang Liang & Sonia Valeria Avilés-Sacoto & Raha Imanirad & Wade D. Cook & Joe Zhu, 2017. "Modeling efficiency in the presence of multiple partial input to output processes," Annals of Operations Research, Springer, vol. 250(1), pages 235-248, March.
    20. Varmaz, Armin & Varwig, Andreas & Poddig, Thorsten, 2013. "Centralized resource planning and Yardstick competition," Omega, Elsevier, vol. 41(1), pages 112-118.
    21. Milioni, Armando Zeferino & de Avellar, José Virgílio Guedes & Gomes, Eliane Gonçalves & Soares de Mello, João Carlos Correia Baptista, 2011. "An ellipsoidal frontier model: Allocating input via parametric DEA," European Journal of Operational Research, Elsevier, vol. 209(2), pages 113-121, March.
    22. Yu, Ming-Miin & Rakshit, Ipsita, 2023. "Target setting for airlines incorporating CO2 emissions: The DEA bargaining approach," Journal of Air Transport Management, Elsevier, vol. 108(C).
    23. Arocena, Pablo & Cabasés, Fermín & Pascual, Pedro, 2022. "A centralized directional distance model for efficient and horizontally equitable grants allocation to local governments," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    24. Liesiö, Juuso & Andelmin, Juho & Salo, Ahti, 2020. "Efficient allocation of resources to a portfolio of decision making units," European Journal of Operational Research, Elsevier, vol. 286(2), pages 619-636.
    25. Yu, Ming-Miin & Chen, Li-Hsueh, 2016. "Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 650-671.

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