Fuzzy resource allocation in two-stage systems

Document Type : Original Article

Authors

Abstract

Organizations and institutions have internal departments that the overall performance of the organization is the result of the performance of each of these departments or stages. Each stage has its own input and output factors as well as connecting factors between stages. The factors between stages are called intermediate indicators. In a two-stage structure, the intermediate indicators are the outputs of the first stage, which are used as the input of the second stage. Evaluation performance of an organization should be determined by considering the performance of each of its stages. Data envelopment analysis is one of the appropriate methods to evaluate performance based on several indicators. In practice, it is not possible to determine these indicators with exact values. In this paper, we proposed a model for determining the performance of two-stager systems in a fuzzy environment, also as well as the allocation of resources to it. We apply inverse data envelopment analysis and propose a multi-objective programming model that, by increasing the output of the unit under evaluation, determines the rate of increase of the first stage inputs and intermediate indicators in a way that maintains its efficiency. Then we illustrate the proposed model to allocate resources to bank branches.

Keywords


[1] Arya, A., & Yadav, S. P. (2018). Development of FDEA models to measure the performanceefficiencies of dmus. International Journal of Fuzzy Systems, 20,163–173.
 
[2] Chen Y, Du J., Sherman H.D., Zhu J. (2010) DEA model with shared resources and efficiency decomposition, European Journal of Operational Research, 207, 339-349.
 
[3] Chen, Y., Cook W.D., Zhu, J., (2010) Deriving the DEA frontier for two-stage processes,European Journal of Operational Research 202, 138–142.
 
[4] Despotis, D. K., Sotiros, D., and Koronakos, G., (2016) A network DEA approach for series multi-stage processes. Omega, 61, 35-48.
 
[5] Ehrgott M. (2005) Multicriteria Optimization. 2nd edition, springer.
 
[6] Ghiyasi, M. (2015) On inverse DEA model: The case of variable returns to scale, ComputersIndustrial Engineering 87, 407–409.
 
[7] Kao, C., (2017) Efficiency measurement and frontier projection identification for general two-stage systems in data envelopment analysis, European Journal of Operational Research, 261, 679 – 689.
 
[8] Kao, C, Liu S-T.(2011) Efficiencies of two-stage systems with fuzzy data. Fuzzy Sets and Systems 176, 20–35.
 
[9] Lertworasirikul S., Charnsethikul P., Fang S.,(2011) Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale, Computers & Industrial Engineering 61, 1017–1023.
 
[10] Liang, L., Cook, W., Zhu, J., (2008) DEA models for two-stage processes: Game approach and efficiency decomposition, Naval Research Logistics 55, 643–653.
 
[11] Payan, A, Noora, A.A, Hosseinzadeh Lotfi, F, Khodabakhshi, A, (2013) Relative Efficiency in Two-Stage DEA and Its Application to Bank Branches, Journal of Basic and Applied Scientific Research, 3(2s) 396-404.
 
[12] Seiford, L.M., Zhu, J., Profitability and marketability of the top 55 US commercial banks,Management Sciences, 45 (1999) 1270-1288.
 
[13] Tavana M., Khalili-Damghani K., Santos Arteaga F., Hosseini A. (2019) A fuzzy multi-objective multi-period network DEA model for efficiency measurement in oil refineries, Computers & Industrial Engineering 135, 143–155.
 
[14] Wang, Y-M., Luo, Y., Liang, L., Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises, Expert Systems with Applications 36 (2009) 5205–5211.
 
[15] Wei, Q., Zhang, J., & Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research,121(1),151–16.
 
[16] Zhu, J. (2000) Multi-factor performance measure model with an application to Fortune 500 companies, European Journal of Operational Research 123, 105-124.