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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Notes
- 1.
The problem instances are publicly available at https://www.ac.tuwien.ac.at/research/problem-instances/#evfcap.
- 2.
Please note that we use a slightly different MILP problem formulation than [9] (we use helper variables for the battery levels), since we noticed that this yields a better performance. With the formulation from [9] the MILP approach is able to find feasible solutions for the larger instances within 60 min, but only the trivial solutions, where no reservation is assigned to an EV.
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Limmer, S., Varga, J., Raidl, G.R. (2023). An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicles. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_1
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DOI: https://doi.org/10.1007/978-3-031-30229-9_1
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