We develop a probability-based multi-path location model for an optimal deployment of alternative fueling stations (AFSs) on a transportation network. Distinct from prior research efforts in AFS problems, in which all demands are deemed as given and fixed, this study takes into account that not every node on the network will be equally probable as a demand node. We explicitly integrates the probability into the model based on the multi-path refueling location model to determine the optimal station locations with the goal to maximize the expected total coverage of all demand nodes on a system level. The resulting mixed integer linear pro- gram (MILP) is NP-hard. A heuristic based on genetic algorithm is developed to overcome the computational challenges. We have conducted extensive numerical experiments based on the benchmark Sioux Falls network to justify the applicability of the proposed model and heuristic solution methods.
@article{arxiv.1805.06068,
title = {A probability-based multi-path alternative fueling station location model},
author = {Shengyin Li and Yongxi Huang},
journal= {arXiv preprint arXiv:1805.06068},
year = {2018}
}