Hybrid rough set and data envelopment analysis approach to technology prioritisation
Abstract
The complexity and speed of change in technological systems pose new challenges to technology management. Particular attention should be given to the issue of modelling the uncertainty of assessments and creating rules for determining the weights of the technology assessment criteria. The article aims to present a comprehensive hybrid technology prioritisation model based on the Data Envelopment Analysis and the concept of Rough Sets. The technology prioritisation process that uses the proposed model includes three consecutive stages: (i) the formulation of technology assessment matrix, (ii) the removal of the criteria redundancy based on indiscernibility relation defined in the Rough Set Theory, (iii) the development of rough variables and prioritisation using the DEA super-efficiency model. The combination of DEA and RS is a unique proposal to classify and rank objects based on the tabular representation of their conditional attributes under circumstances of uncertainty. Application of the developed hybrid model to the real data of the technology foresight project “NT FOR Podlaskie 2020” positively verified the assumed effects of its use. The obtained results allow a more objective and rational justification of the chosen technology, simplification of interpretation and better authentication of results from the perspective of decision-makers.
First published online 8 May 2020
Keyword : Data Envelopment Analysis (DEA), Rough Sets (RS), hybrid model, technology prioritisation, technology management, technology assessment
How to Cite
Chodakowska, E., & Nazarko, J. (2020). Hybrid rough set and data envelopment analysis approach to technology prioritisation. Technological and Economic Development of Economy, 26(4), 885-908. https://doi.org/10.3846/tede.2020.12538
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Chodakowska, E., & Nazarko, J. (2017b). Network DEA models for evaluating couriers and messengers. Procedia Engineering, 182, 106–111. https://doi.org/10.1016/j.proeng.2017.03.130
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Fodor, J., & Roubens, M. (1994). Fuzzy preference modelling and multicriteria decision support. Kluwer. https://doi.org/10.1007/978-94-017-1648-2
Gordon, T. J., & Glenn, J. C. (2004, May 13–14). Integration, comparisons, and frontier of futures research methods. In EU-US Seminar: New Technology Foresight, Forecasting & Assessment Methods, Seville, Spain.
Greco, S., Matarazzo, B., & Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1–47. https://doi.org/10.1016/S0377-2217(00)00167-3
Halicka, K. (2016). Innovative classification of methods of the future-oriented technology analysis. Technological and Economic Development of Economy, 22(4), 574–597. https://doi.org/10.3846/20294913.2016.1197164
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