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Joint optimization of heterogeneous charging infrastructure location, heterogeneous electric bus routing and charging scheduling

https://doi.org/10.37661/1/1816-0301-2026-23-3-24-48

Abstract

Objectives. When replacing diesel buses with electric buses, the problem of selecting the composition of the electric bus fleet, assigning service trips from a given set to electric buses, and determining charging infrastructure and charging schedules becomes relevant. The aim of the study is to create models and methods for obtaining cost-effective solutions to this problem for heterogeneous electric buses and charging stations under different passenger flow volumes for different trips.

Methods. Methods of set theory, graph theory, operations research, and mathematical programming are used.

Results. A mathematical model has been developed for the problem of optimizing a fleet of various-type electric buses, their routing, assignment to depots at the beginning and end of the planning interval, determination of the locations and number of charging stations of different types at route terminals, and charging schedules. The objective function is the total daily capital and operating costs of electric buses, charging stations, and consumed electricity. A two-level decomposition scheme for solving the problem is proposed, at the upper level of which daily assignments for electric buses are determined (assigning different types of electric buses to service trip sequences with varying passenger volumes). The lower level distributes electric buses among depots, locates charging stations at route terminals, and determines charging schedules for electric buses with fixed trip assignments. A heuristic local search algorithm is proposed for solving the upper-level subproblem, and a mixed integer linear programming model is developed for the lower-level subproblem.

Conclusion. The solution to the upper-level subproblem consists of assigning different types of electric buses to service trips with varying passenger volumes. Standard solvers, such as Gurobi Optimizer, can be used to solve the formulated lower-level subproblem.

About the Authors

N. N . Guschinsky
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Nikolai N . Guschinsky, Cand. Sci. (Phys.-Math.), Assoc. Prof., Leading Researcher

st. Surganova, 6, Minsk, 220012



M. Y. Kovalyov
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Mikhail Y. Kovalyov, Corresponding Member of the National Academy of Sciences of Belarus, Dr. Sci. (Phys.-Math.), Prof., Principal Researcher

st. Surganova, 6, Minsk, 220012



B. M. Rozin
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Boris M. Rozin, Cand. Sci. (Eng.), Assoc. Prof., Leading Researcher

st. Surganova, 6, Minsk, 220012



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For citations:


Guschinsky N.N., Kovalyov M.Y., Rozin B.M. Joint optimization of heterogeneous charging infrastructure location, heterogeneous electric bus routing and charging scheduling. Informatics. 2026;23(3):24-48. (In Russ.) https://doi.org/10.37661/1/1816-0301-2026-23-3-24-48

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ISSN 1816-0301 (Print)
ISSN 2617-6963 (Online)