Record link: 
http://hdl.handle.net/20.500.12708/192522
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Title: 
An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicles
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Citation: 
Limmer, S., Varga, J., & Raidl, G. R. (2023). An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicles. In J. G. M. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation : 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (pp. 3–18). Springer. https://doi.org/10.1007/978-3-031-30229-9_1
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Publisher DOI: 
10.1007/978-3-031-30229-9_1
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Limmer, Steffen 
Varga, Johannes 
Raidl, Günther R. 
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Organisational Unit: 
E192-01 - Forschungsbereich Algorithms and Complexity 
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Published in: 
Applications of Evolutionary Computation : 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings 
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ISBN: 
978-3-031-30229-9
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Volume: 
13989
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Date (published): 
2023
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Event name: 
26th European Conference, EvoApplications 2023 
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Event date: 
12-Apr-2023 - 14-Apr-2023
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Event place: 
Brno, Czechia
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Number of Pages: 
16
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
Electric vehicles; eMaaS; Evolutionary algorithm; Mixed integer linear programming; Scheduling
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Abstract: 
In the present paper, we investigate the management of a fleet of electric vehicles. We propose a hybrid evolutionary approach for solving the problem of simultaneously planning the charging of electric vehicles and the assignment of electric vehicles to a set of reservations. The reservation assignment is optimized with an evolutionary algorithm while linear programming is used to compute optimal charging schedules. The evolutionary algorithm uses an indirect encoding and a problem-specific crossover operator. Furthermore, we propose the use of a surrogate fitness function. Experimental results on problem instances with up to 100 vehicles and 1600 reservations show that the proposed approach is able to notably outperform two approaches based on mixed integer linear programming.
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
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