A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives
M. Tavana, D. Di Caprio, F.J. Santos-Arteaga, Information Sciences 381 (2017) 322–340.
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Journal Article
| Published
| English
Author
Tavana, MadjidLibreCat;
Di Caprio, Debora;
Santos-Arteaga, Francisco J.
Publishing Year
Journal Title
Information Sciences
Volume
381
Page
322-340
ISSN
LibreCat-ID
Cite this
Tavana M, Di Caprio D, Santos-Arteaga FJ. A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives. Information Sciences. 2017;381:322-340. doi:10.1016/j.ins.2016.11.021
Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2017). A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives. Information Sciences, 381, 322–340. https://doi.org/10.1016/j.ins.2016.11.021
@article{Tavana_Di Caprio_Santos-Arteaga_2017, title={A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives}, volume={381}, DOI={10.1016/j.ins.2016.11.021}, journal={Information Sciences}, publisher={Elsevier BV}, author={Tavana, Madjid and Di Caprio, Debora and Santos-Arteaga, Francisco J.}, year={2017}, pages={322–340} }
Tavana, Madjid, Debora Di Caprio, and Francisco J. Santos-Arteaga. “A Multi-Criteria Perception-Based Strict-Ordering Algorithm for Identifying the Most-Preferred Choice among Equally-Evaluated Alternatives.” Information Sciences 381 (2017): 322–40. https://doi.org/10.1016/j.ins.2016.11.021.
M. Tavana, D. Di Caprio, and F. J. Santos-Arteaga, “A multi-criteria perception-based strict-ordering algorithm for identifying the most-preferred choice among equally-evaluated alternatives,” Information Sciences, vol. 381, pp. 322–340, 2017, doi: 10.1016/j.ins.2016.11.021.
Tavana, Madjid, et al. “A Multi-Criteria Perception-Based Strict-Ordering Algorithm for Identifying the Most-Preferred Choice among Equally-Evaluated Alternatives.” Information Sciences, vol. 381, Elsevier BV, 2017, pp. 322–40, doi:10.1016/j.ins.2016.11.021.