Robust Charging Network Planning for Metropolitan Taxi Fleets

Gregor Godbersen (), Rainer Kolisch () and Maximilian Schiffer ()
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Gregor Godbersen: School of Management, Department of Operations & Technology, Technical University of Munich, 80333 Munich, Germany
Rainer Kolisch: School of Management, Department of Operations & Technology, Technical University of Munich, 80333 Munich, Germany
Maximilian Schiffer: School of Management, Department of Operations & Technology, Technical University of Munich, 80333 Munich, Germany; Munich Data Science Institute, Technical University of Munich, 80333 Munich, Germany

Transportation Science, 2024, vol. 58, issue 2, 295-314

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 make 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 citywide 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 shows a more favorable impact on vehicle feasibility of up to 10 percentage points compared with 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%.

Keywords: charging infrastructure design; adjustable robust optimization; cutting plane method; branch and price; electric vehicles (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:58:y:2024:i:2:p:295-314

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