Efficient solution approaches for locating electric vehicle fast charging stations under driving range uncertainty

Mouna Kchaou Boujelben, Celine Gicquel

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

We seek to determine the best locations for electric vehicle fast charging stations under driving range uncertainty. Two stochastic programming based models have been recently proposed to handle the resulting stochastic flow refueling location problem: a first one maximizing the expected flow coverage of the network, a second one based on joint chance constraints. However, significant computational difficulties were encountered while solving large-size instances. We thus propose two efficient solution approaches for this problem. The first one is based on a new location-allocation type model for this problem and results in a MILP formulation, while the second one is a tabu search heuristic. Our numerical experiments show that when using the proposed MILP formulation, the computation time needed to provide guaranteed optimal solutions is significantly reduced as compared to the one needed when using the previously published MILP formulation. Moreover, our results also show that the tabu search method consistently provides good quality solutions within short computation times.

Original languageEnglish
Pages (from-to)288-299
Number of pages12
JournalComputers and Operations Research
Volume109
DOIs
Publication statusPublished - Sep 2019

Keywords

  • Electric vehicle charging station network design
  • Flow refueling location model
  • Mixed-integer linear programming
  • Stochastic driving range
  • Stochastic programming
  • Tabu search

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research

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