Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations

Abstract

Electric vehicles are the key to facilitating the transition to low-carbon ‘green’ transport. However, there are concerns with their range and the location of the charging stations which delay a full-fledged adoption of their use. Hence, the electric charging infrastructure in a given region is critical to mitigating those concerns. In this study, an interval type-2 fuzzy set based multi-criteria decision-making method is introduced for selecting the best location for electric charging stations. This method is improved by Simulated Annealing obtaining the best configuration of the parameters of the interval type-2 membership functions along with two different aggregation operators; linguistic weighted sum and average. The proposed overall reusable multi-stage solution approach is applied to a real-world public transport problem of the municipal bus company in Istanbul. The results indicate that the approach indeed improves the model, capturing the associated uncertainties embedded in the interval type-2 membership functions better, leading to a more effective fuzzy system. The experts confirm those observations and that Simulated Annealing improved interval type-2 fuzzy method achieves more reliable results for selecting the best sites for the electric bus charging stations.

Citation

Türk, S., Deveci, M., Özcan, E., Canıtez, F., & John, R. (2021). Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations. Information Sciences, 547, 641-666. https://doi.org/10.1016/j.ins.2020.08.076

• Journal Article Type: Article
• Acceptance Date: Aug 23, 2020
• Online Publication Date: Sep 1, 2020
• Publication Date: Feb 8, 2021
• Deposit Date: Dec 2, 2021
• Publicly Available Date: Dec 9, 2021
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 547
• Pages: 641-666
• DOI: https://doi.org/10.1016/j.ins.2020.08.076
• Keywords: Control and Systems Engineering; Theoretical Computer Science; Software; Information Systems and Management; Artificial Intelligence; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/4948923
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0020025520308513?via%3Dihub