Robust Charging Network Planning for Metropolitan Taxi Fleets

TL;DR

This paper develops a robust optimization framework for planning large-scale taxi fleet charging stations in cities, balancing cost, risk, and technological factors to ensure operational feasibility.

Contribution

It introduces a novel solution method combining location routing and flow refueling problems, with a robust framework for city-wide taxi fleet charging station planning.

Findings

Increasing battery capacity improves vehicle feasibility more than faster charging speeds.

Depot charging significantly enhances operational feasibility.

Reducing infeasibility risk lowers infrastructure costs by 20%.

Abstract

We study the robust charging station location problem for a large-scale commercial taxi fleet. Vehicles within the fleet coordinate on charging operations but not on customer acquisition. We decide on a set of charging stations to open to ensure operational feasibility. To take this decision, we propose a novel solution method situated between the Location Routing Problems with Intraroute Facilities and Flow Refueling Location Problems. Additionally, we introduce a problem variant that makes a station sizing decision. Using our exact approach, charging stations for a robust operation of city-wide taxi fleets can be planned. We develop a deterministic core problem employing a cutting plane method for the strategic problem and a branch-and-price decomposition for the operational problem. We embed this problem into a robust solution framework based on adversarial sampling, which allows for planner-selectable risk tolerance. We solve instances derived from real-world data of the metropolitan area of Munich, containing 1,000 vehicles and 60 potential charging station locations. Our investigation of the sensitivity of technological developments shows that increasing battery capacities show a more favorable impact on vehicle feasibility of up to 10 percentage points compared to increasing charging speeds. Allowing for depot charging dominates both of these options. Finally, we show that allowing just 1% of operational infeasibility risk lowers infrastructure costs by 20%.