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Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: The limitation of the study is that only one DMU can expect to benefit from the application of these methods at any given time. Practical implications: 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. Originality/value: 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. © 2016, © Emerald Group Publishing Limited.

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Decision Sciences, Operations Management, and Information Systems

(SME) School of Management and Entrepreneurship

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Input/output weight restrictions, CSOI constraint and efficiency improvement

This paper proposes, Data Envelopment Analysis (DEA) models and algorithms to reduce inputs and increase outputs for an inefficient Decision Making Unit (DMU) in a network DEA. Inputs and outputs considered for change are under constant sum constraint. In a network, DMU composed of multiple divisions, it is possible that changes in inputs and outputs of one division may affect the efficiency of other divisions. At the same time, it is also possible to change the efficiency of a division without changing the efficiency of the entire network. Models to reallocate inputs/outputs under constant sum constraint need to take these possibilities into account. The reallocation of inputs or outputs is done without reducing the network DEA efficiency of the other DMUs. Models and algorithms have been developed to transfer intermediate products from one DMU to another under constant sum constraint. These models and algorithms will help the decision maker to allocate resources more effectively among various divisions in order to improve the efficiency of the system consisting of these interrelated divisions. Theoretical results have been illustrated with the help of a numerical example. © 2015 IEEE.

IEOM 2015 - 5th International Conference on Industrial Engineering and Operations Management, Proceeding

Network DEA efficiency improvement under constant sum of inputs/outputs

In this paper, we have formulated Data Envelopment Analysis (DEA) models to reduce the inputs in an inefficient Decision Making Unit (DMU) when the specific inputs are under the constant sum constraint. We have also extended the models to reallocate the excess input without any reduction in efficiency of other DMUs. These DEA models and methods developed in this work will help decision makers in developing an optimal strategy to transfer excess input to other DMUs. Theoretical results have been illustrated with the help of a case study. © Springer Science+Business Media Dordrecht 2014.