Surya Sarathi Majumdar

Bio: Surya Sarathi Majumdar is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Data envelopment analysis & Input/output. The author has an hindex of 1, co-authored 3 publications receiving 5 citations. Previous affiliations of Surya Sarathi Majumdar include Indian Institute of Management Calcutta.

Efficiency improvement strategy under constant sum of inputs

Abstract: 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.

Network DEA efficiency improvement under constant sum of inputs/outputs

Abstract: 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.

Input/output weight restrictions, CSOI constraint and efficiency improvement

Abstract: 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.

Optimal Path for Controlling Sectoral CO2 Emissions Among China’s Regions: A Centralized DEA Approach

Abstract: This paper proposes a centralized data envelopment analysis (DEA) model for industrial optimization based on several different production technologies among several regions. We developed this model based on improved Kuosmanen environmental DEA technology, which avoids positive shadow price on undesirable outputs. We also designed a dual model for our centralized DEA model, and used it to analyze shadow prices on CO2 emissions. We further employed the proposed model to determine the optimal path for controlling CO2 emissions at the sector level for each province in China. At sectoral level, manufacturing showed the highest potential emissions reduction, and transportation was the largest accepter of emission quotas. At regional level, western and northeastern areas faced the largest adjustments in allowable emissions, while central and eastern areas required the least amount of adjustment. Because our model represents increase or decrease in emissions bidirectionally in terms of shadow price analysis, this setting makes the shadow price on CO2 emissions lower than strong regulation (decreasing CO2 emissions along with increasing value added) used by directional distance function (DDF).

Performance and competition analysis with fixed-sum measures : A case on OPEC members

Abstract: The problem of constructing a common equilibrium efficient frontier with fixed-sum inputs or outputs is studied in this paper. To do this, all decision making units are classified into different cla...

Network DEA efficiency improvement under constant sum of inputs/outputs

Abstract: 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.

Input/output weight restrictions, CSOI constraint and efficiency improvement

Abstract: 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.