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DEA Models with Undesirable Inputs, Intermediates, and Outputs

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Book cover Data Envelopment Analysis

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 221))

Abstract

In real applications involving the use of Data Envelopment Analysis (DEA) models, undesirable inputs and outputs have been frequently encountered and addressed, e.g., via data transformation. These studies were scattered in the literature and often confined to some particular applications. In this paper, we present a systematic investigation concerning the building of DEA models. First, we describe the desirability of inputs and outputs, as well as the disposability assumptions in the presence of undesirable inputs and outputs. Next we construct a number of DEA models with different disposability assumptions and performance measures for the case of single-stage DEA. Next, we try to systematically investigate two-stage DEA models with undesirable inputs, intermediates and outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets and the corresponding DEA models with undesirable inputs, intermediates, and outputs.

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Acknowledgement

This research is supported by the National Natural Science Foundation of China (No. 71371067, 71201158), Chinese Postdoctoral Science Foundation, Hunan Provincial Foundation for Social Sciences of China (No. 09YBB073).

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Authors and Affiliations

  1. School of Business Administration, Hunan University, 410082, Changsha, China

    Zhongbao Zhou & Wenbin Liu

  2. Kent Business School, University of Kent, CT2 7PE, Canterbury, UK

    Wenbin Liu

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  1. Zhongbao Zhou
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  2. Wenbin Liu
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Correspondence to Wenbin Liu .

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Editors and Affiliations

  1. Department of Management, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Joe Zhu

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Zhou, Z., Liu, W. (2015). DEA Models with Undesirable Inputs, Intermediates, and Outputs. In: Zhu, J. (eds) Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 221. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7553-9_15

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