Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

no code implementations • 4 Aug 2020 • Yuzhu Wu, Zhen Zhang, Gang Kou, Hengjie Zhang, Xiangrui Chao, Cong-Cong Li, Yucheng Dong, Francisco Herrera

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making.

Attribute Decision Making +1

On scenario construction for stochastic shortest path problems in real road networks

no code implementations • 1 Jun 2020 • Dongqing Zhang, Stein W. Wallace, Zhaoxia Guo, Yucheng Dong, Michal Kaut

We find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i. e., relative difference and variance) whichever origin-destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6-10 times lower for a stability level of 1\%; and (3) different origin-destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.

Computational Efficiency