Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54480288
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2018.06.006
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 248
- Volume
- 262
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This establishes the first optimal and anytime algorithms for electric vehicle scheduling for mobility-on-demand schemes e.g., run by ZipCar. It was chosen as one of the top articles by the AI journal for its impact (https://bit.ly/2T6Tucg). The work led to additional funding to develop an open-source simulation tool (10.1016/j.simpat.2018.06.007) and follow-on funding for the first author to pursue post-doctoral research at the University of Thessaloniki.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -