Abstract
This chapter aims to present a newly developed and extended distance friction minimization (DFM) model in the context of Data Envelopment Analysis (DEA), in order to comply with plausible and real-world circumstances. The DFM model is generally able to calculate either an optimal input reduction value or an output increase value in order to reach an efficiency score of 1.000, even though in reality this might be hard to achieve for low-efficiency DMUs. Most DEA models and also the DFM model have intrinsic limitations or weaknesses. Therefore, we need a method that allows for the presence of reference points that remain below the efficiency frontier. In this chapter we propose successively a Goals-Achievement model, a Stepwise Improvement model, and a Target-Oriented model based on the DFM framework. These models are categorized as “Target approaches.” On the other side, in many cases, input or output factors may not be directly flexible or adjustable due to the indivisible nature or inertia in some input or output factors. Usually, the original DEA model and the DFM model do not allow for such a non-controllable or a fixed input factor. Therefore, we need a method that may take into account a flexible or adjustable factor in a DFM model. In this chapter, we propose an Adjusted-Improvement model and a Fixed-Factor model based on the DFM framework. These models are categorized as “Adjustment approaches.”
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Andersen, P., & Petersen, N. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261–1264.
Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513–521.
Nijkamp, P., & Suzuki, S. (2009). A generalized goals-achievement model in data envelopment analysis: An application to efficiency improvement in local government finance in Japan. Spatial Economic Analysis, 4(3), 249–274.
Seiford, L. M., & Zhu, J. (2003). Context-dependent data envelopment analysis -measuring attractiveness and progress. Omega, 31, 397.
Suzuki, S., & Nijkamp, P. (2011). A stepwise-projection data envelopment analysis for public transport operations in Japan. Letters in Spatial and Resource Sciences, 4(2), 139–156.
Suzuki, S., & Nijkamp, P. (2014). A stepwise efficiency improvement DEA model for airport management with a fixed runway capacity, Karlsruhe papers in economic policy research. Volucella, 34, 233–254.
Suzuki, S., & Nijkamp, P. (2016). An evaluation of energy-environment-economic efficiency for EU APEC and ASEAN countries: Design of a target-oriented DFM model with fixed factors in data envelopment analysis. Energy Policy, 88, 100–112.
Suzuki, S., Nijkamp, P., & Rietveld, P. (2011). Regional efficiency improvement by means of data envelopment analysis through Euclidean distance minimization including fixed input factors: An application to tourist regions in Italy. Papers in Regional Science, 90(1), 67–89.
Suzuki, S., Nijkamp, P., & Rietveld, P. (2015). A target-oriented data envelopment analysis for energy-environment efficiency improvement in Japan. Energy Efficiency, 8(3), 433–446.
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Suzuki, S., Nijkamp, P. (2017). Extended DFM Models in DEA. In: Regional Performance Measurement and Improvement. New Frontiers in Regional Science: Asian Perspectives, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-0242-7_5
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DOI: https://doi.org/10.1007/978-981-10-0242-7_5
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