The fluvial passenger transport design problem with an electric boat

In this paper, we introduce a new optimization problem called the fluvial passenger transport design problem with an electric boat (FPTDP-EB). This problem is inspired by a future fluvial operation using electric boats (EBs) for passenger transportation between two cities on the banks of the Magdalena River in Colombia. The FPTDP-EB consists of selecting the battery capacity for the EB, the charging infrastructure to install, and the scheduling of each round trip. For the charging infrastructure, we considered the location of the charging stations, their charging powers, and whether they would have photovoltaic (PV) components. For stations with PV components, we also considered the number of PV panels and the capacity of their energy storage systems. For this problem, we considered the stochastic behavior of solar irradiance, the arrival times of candidate passengers for the EB, and power outages for the electric grid. The objective function is to minimize the operation's investment cost, the maintenance cost of the charging stations and the cost of the energy to be purchased from the grid during a time horizon. We propose a branch-and-bound algorithm with a Monte Carlo simulation to solve the FPTDP-EB. The latter evaluates the feasibility of each solution and estimates the amount of energy to be purchased from the grid during the time horizon. Our method solves the operation design for the Magdalena River scenario in reasonable computation times for a strategic problem. Additionally, we perform some sensitivity analyses to evaluate how certain factors, such as the energy density of batteries, could impact the structure of the solution. Our results show that the use of photovoltaic charging stations helps minimizing the overall cost of the operation, and makes it more resilient towards power outages. Additionally, as the energy density of electric batteries continues improving, the overall cost of these type of operations is expected to decrease significantly.