@article{cokyasar_designing_2021-1,
	title = {Designing a drone delivery network with automated battery swapping machines},
	volume = {129},
	issn = {0305-0548},
	url = {https://www.sciencedirect.com/science/article/pii/S030505482030294X},
	doi = {10.1016/j.cor.2020.105177},
	abstract = {Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery.},
	language = {en},
	urldate = {2023-08-01},
	journal = {Computers \& Operations Research},
	author = {Cokyasar, Taner and Dong, Wenquan and Jin, Mingzhou and Verbas, İsmail Ömer},
	month = may,
	year = {2021},
	keywords = {Drone delivery, Mixed-integer nonlinear programming, Network optimization, Queueing theory},
	pages = {105177},
}

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We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). 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This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). 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