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A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs

Author

Listed:
  • Xinna Mao
  • Zhao Guoxi
  • Mohammad Fallah
  • S. A. Edalatpanah

Abstract

Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may additionally be present in the manufacturing system, which wishes to be minimized. To tackle the mentioned issues and in order to obtain a reliable measurement that keeps original advantage of DEA and considers the influence of undesirable factors under the indeterminate environments, this paper presents a neutrosophic DEA model with undesirable outputs. The recommended technique is based on the aggregation operator and has a simple construction. Finally, an example is given to illustrate the new model and ranking approach in details.

Suggested Citation

  • Xinna Mao & Zhao Guoxi & Mohammad Fallah & S. A. Edalatpanah, 2020. "A Neutrosophic-Based Approach in Data Envelopment Analysis with Undesirable Outputs," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-8, July.
    • Handle: RePEc:hin:jnlmpe:7626102
    DOI: 10.1155/2020/7626102
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    Cited by:

    1. Lenin Kanagasabai, 2022. "Novel Western Jackdaw search, antrostomus swarm and Indian ethnic vedic teaching: inspired optimization algorithms for real power loss diminishing and voltage consistency growth," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2895-2919, December.
    2. Lenin Kanagasabai, 2022. "Buoyancy based optimization algorithm for real power loss diminution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2442-2457, October.
    3. Lenin Kanagasabai, 2023. "Real power loss reduction by extreme learning machine based Panthera leo, chaotic based Jungle search and Quantum based Chipmunk search optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 55-78, March.

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