Optimal placement of electric vehicle slow-charging stations: A continuous facility location problem under uncertainty

Electric vehicles (EVs) are becoming a key mechanism to reduce emissions in the transportation industry, and hence contribute to the green transition. In this paper, we present a mathematical programming model which determines the optimal placement of EV charging stations such that chargers are placed in the most cost-efficient way possible for all stakeholders, assuming additionally that EV charging demand is inherently stochastic in nature. The model is formulated as a two-stage, continuous location–allocation model in the form of a generalised Weber problem in two dimensions. However, this formulation is non-convex and notoriously difficult to solve. We therefore propose a suitable discretisation procedure to find high quality solutions in suitable time. The discretisation procedure shows strong performance across a variety of computational experiments using randomly generated scenarios, maintaining robustness in terms of the objective value and overall solution quality.<br/><br/>A part of this solution procedure was entered into the 15th AIMMS-MOPTA Optimisation Modelling Competition.

Publication Details

Journal
Computers & Operations Research
Published
2025-09-27
DOI
https://doi.org/10.1016/j.cor.2025.107289
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Optimal placement of electric vehicle slow-charging stations: A continuous facility location problem under uncertainty

Computers & Operations Research
Geochemistry and Geologic Mapping
article

Optimal placement of electric vehicle slow-charging stations: A continuous facility location problem under uncertainty

article en

Abstract

Electric vehicles (EVs) are becoming a key mechanism to reduce emissions in the transportation industry, and hence contribute to the green transition. In this paper, we present a mathematical programming model which determines the optimal placement of EV charging stations such that chargers are placed in the most cost-efficient way possible for all stakeholders, assuming additionally that EV charging demand is inherently stochastic in nature. The model is formulated as a two-stage, continuous location–allocation model in the form of a generalised Weber problem in two dimensions. However, this formulation is non-convex and notoriously difficult to solve. We therefore propose a suitable discretisation procedure to find high quality solutions in suitable time. The discretisation procedure shows strong performance across a variety of computational experiments using randomly generated scenarios, maintaining robustness in terms of the objective value and overall solution quality.<br/><br/>A part of this solution procedure was entered into the 15th AIMMS-MOPTA Optimisation Modelling Competition.

Computers & Operations ResearchVol. 185
Openalex Percentile: Top 26%
Geochemistry and Geologic Mapping
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