The purpose of this paper is to develop data envelopment analysis (DEA) models and algorithms for efficiency improvement when the inputs and output weights are restricted and there is fixed availability of inputs in the system.
Limitation on availability of inputs is represented in the form of constant sum of inputs (CSOI) constraint. The amount of excess input of an inefficient decision-making unit (DMU) is redistributed among other DMUs in such a way so that there is no reduction in their efficiency. DEA models have been developed to design the optimum strategy to reallocate the excess input.
The authors have developed the method for reallocating the excess input among DMUs while under CSOI constraint and parameter weight restrictions. It has been shown that in this work to improve the efficiency of an inefficient DMU one needs the cooperation of selected few DMUs. The working of the models and results have been shown through a case study on carbon dioxide emissions of 32 countries.
The limitation of the study is that only one DMU can expect to benefit from the application of these methods at any given time.
Results of the paper are useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance.
This strategy of reallocation of excess input will be very useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance. Unlike the existing works on efficiency improvement under CSOI, this work seeks to address the issue of efficiency improvement when the input/output parameter weights are also restricted.
The authors wish to express their deep gratitude to the anonymous reviewers for their comments and valuable suggestions which have helped to improve the quality of the paper.
Singh, S. and Majumdar, S. (2016), "Input/output weight restrictions, CSOI constraint and efficiency improvement", Benchmarking: An International Journal, Vol. 23 No. 7, pp. 2080-2091. https://doi.org/10.1108/BIJ-08-2014-0075Download as .RIS
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