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An Inverse Prospect Theory Based-Approach for Linear Ordinal Ranking Aggregation with Its Application in Site Selection of Electric Vehicle Charging Station

Liu, Nana and Xu, Zeshui and Wu, Hangyao and Ren, Peijia and Meng, Fanlin (2020) An Inverse Prospect Theory Based-Approach for Linear Ordinal Ranking Aggregation with Its Application in Site Selection of Electric Vehicle Charging Station. In: IJCNN 2020: International Joint Conference on Neural Networks, 2020-07-19 - 2020-07-24, Glasgow, UK.

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Abstract

Considering that it is difficult for experts to provide precise preference values for the site selection of electric vehicle charging station in risky environment, this paper develops an approach for linear ordinal ranking aggregation to validly improve the efficiency and accuracy of electric vehicle charging station site selection. At first, the inverse value function of prospect theory is applied to reduce the impact of risk. Then, through combining with the concept of information energy, the experts' weights can be derived. Besides, a consistency constraint is added to the individual ranking-based alternatives' weights deriving model, which can guarantee the consistency degree at an acceptable level. Additionally, a consensus and standard deviation-based model is established to aggregate the alternatives' weights. Finally, a numerical case about the electric vehicle charging station site selection is presented to show the usage of the approach, meanwhile, comparative analysis and sensitivity analysis are also conducted which show the robustness and practicability of the approach.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 08 Jul 2021 15:27
Last Modified: 08 Jul 2021 15:27
URI: http://repository.essex.ac.uk/id/eprint/30718

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