Abstract
Conventional data envelopment analysis (DEA) is a non-parametric approach to examine the efficiency of similar decision-making units (DMUs) as a whole system without considering its internal structure, i.e., the system is considered as a black box. Therefore, a network DEA (NDEA) is needed to study the internal structure of a system. By allowing for the measurement of individual components, an NDEA model can reveal inefficiencies that a traditional DEA ignores. In this study, we use the parallel network DEA to calculate the efficiency of a higher education institute with 19 decision-making units (DMUs) using two parallel processes (teaching and research) and compare it with the conventional DEA, CCR model through a numerical example. The main advantages of the parallel NDEA model are (i) to identify which DMUs are inefficient and make necessary adjustments, and (ii) the parallel DEA model has a lower efficiency score than the traditional DEA model.
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Kumar, A., Panwar, A., Pant, M. (2023). Efficiency Assessment of an Institute Through Parallel Network Data Envelopment Analysis. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_45
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