Efficiency Evaluation of Rental Vehicle Usability in Undesirable Factor Model for Data Envelopment Analysis
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Published:
Dec 25, 2018
Keywords:
Efficiency evaluation Undesirable factor Data envelopment analysis
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Abstract
The objective of this research is to study and compare the different methods used in data envelopment analysis (DEA) for evaluating efficiency of rental vehicle usability when undesirable factors exist. In this research, data on the use of rental vehicles taken from the 22 government agencies are referred to as the Decision Making Unit (DMU). Such data include the cost of car rentals as an input factor, the entire active distance and the cost of maintenance as a desirable output, and the cost of car repairing after accident as an undesirable output; all mentioned information were gathered from the Information Systems (IS) of government agencies and car rental companies. To evaluate the efficiency on the use of rental vehicles, three methods (i.e. multiplicative inverse method, undesirable outputs as inputs method, and additive inverse method) were used for data evaluation in the DEA model with undesirable factors and the average efficiency scores of all three methods being comparatively analyzed by Analysis of Variance (ANOVA). The results indicate that there is no significant difference (p > 0.05) of the average efficiency scores as evaluated by three different methods. Therefore, it can be concluded that all three methods used in this research are applicable for DEA when evaluating the efficiency in DMU with undesirable factors. Nevertheless, the situations and relevant factors that may be different in each agency should be taken into consideration.
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References
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Wu, J., Zhu, Q., Chu, J., Liu, H., and Liang, L. (2016). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460-472.
Zhou, P., and Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.
Azadi, M., Shabani, A., Khodakarami, M., and Saen, R. F. (2014). Planning in feasible region bytwo-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers. Transportation Research Part E: Logistics and Transportation Review, 70, 324-338.
Balezentis, A. and Balezentis, T. (2011). Assessing the efficiency of Lithuanian Transport sector by applying the methods of multimoora and data envelopment analysis. Transport, 26(3), 263-270.
Berg, S.A., Forsund, F.R., and Jansen, E.S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian Banking 1980-89. The Scandinavian Journal of Economics, 94, 211-228.
Bian, Y., Hu, M., and Xu, H. (2015). Measuring efficiencies of parallel systems with shared inputs/ outputs using data envelopment analysis. Kybernetes, 44(3), 336-352.
Bowlin, W. F. (1987). Evaluation the efficiency of US Air-Force real property maintenance activities. The Journal of the Operational Research Society, 38(2), 127-135.
Charnes, A., Clark, C. T., Cooper, W. W., and Golany, B. (1984). A developmental study of Data Envelopment Analysis in measuring the efficiency of maintenance units in the U.S. Air Forces. Annals of Operations Research, 2(1), 95-112.
Charnes, A., Cooper, W. W., Lewin, A. Y., and Seiford, L. M. (1994). Basic SEA Models. In Data Envelopment Analysis Theory, Methodology and Applications, pp. 23-47. Dordrecht: Springer.
Charnes, A., Cooper, W.W and Rhodes, E. (1978). Measurement the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
Cook, D. W. (2006). Qualitative Data in Dea. Handbook on Data Envelopment Analysis , 153-175. Boston, MA.: Springer.
Daraio, C., Diana, M., Costa, F. D., Leporelli, C., Matteucci, G., and Nastasi, A. (2016). Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research. European Journal of Operational Research, 248(1), 1–20.
Djordjevic, B., Krmac, E., and Mlinaric, T. J. (2018). Non-radial DEA model: A new approach to evaluation of safety at railway level crossings. Safety Science, 103, 234-246.
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(30), 253-278.
Golany, B. and Roll, Y. (1989). An application Procedure for DEA. Omega: The International Journal of Management Science, 17, 237-250.
Jiang, G., Liu, L., and Lv, H. (2017). Transportation system evaluation model based on DEA. Journal of Discrete Mathematical Sciences and Cryptography, 20(1), 115-124.
Koopmans, T. C. (1951). Activity Analysis of Production and Allocation. New York: Wiley.
Krai-u., M. (2009). Satisfaction of passengers on using air-conditioned van service.
Case Study: Bangkok - Phetchaburi (Line 73). Master of Business Administration, Bangkok University. (in Thai)
Liu, W. and Sharp, J. (1999). DEA models via goal programming. In Data Envelopment Analysis in the Service Sector, 79-101. Wiesbaden: Deutscher Universitätsverlag.
Lovell, C.A.K., Pastor, J.T. and Turner, J.A. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European Journal of Operational Research, 87, 507-518.
Ministry of Finance. (2012). Ministry of Finance Report Urgent GK 0406.4/v.64 Rental rates for government cars, 1-3. (in Thai)
Nimmalairat., C. (2011). The technology Efficiency of Refinery in Thailand. Master of Economic, Srinakharinwiroj University. (in Thai)
Pal, D., and Mitra, S. K. (2016). An Application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India. Transportation Research Part A: Policy and Practice, 93, 1-12.
Pina, V., and Torres, L. (2006). Public–private efficiency in the delivery of services of general economic interest: The case of urban transport. Local Government Studies, 32(2), 177 – 198.
Royston, J.P. (1983). Some techniques for assessing multivariate normality based on the
Shapiro-Wilk W. Applied Statistics, 32, 121-133.
Sanonil., S. (2016). Car Rental Behavior of Foreign Tourists in Pattaya. Master of Business Administration, Burapha University. (in Thai)
Scheel, H. (2001).Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400-410.
Seiford, L. M. and Zhu, J. (2002). Modeling Undesirable factors in efficiency evaluation. European Journal of Operational Research, 142, 16-20.
Sharma, M. G., Debnath, R. M., Oloruntoba, R., and Sharma, S. M. (2016). Benchmarking of rail transport service performance through DEA for Indian railways. The International Journal of Logistics Management, 27(3), 629-649.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.
Wojcik, V., Dyckhoff, H. and Gutgesesell, S. (2017). The desirable Input of Undesirable Factors in Data Envelopment Analysis. Annals of Operation Research, 259(1-2), 461-484.
Wu, J., Zhu, Q., Chu, J., Liu, H., and Liang, L. (2016). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460-472.
Zhou, P., and Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.