Designing a drone delivery network with automated battery swapping machines

Cokyasar, Taner; Dong, Wenquan; Jin, Mingzhou; Verbas, İsmail Ömer

doi:10.1016/j.cor.2020.105177

Cited by 29 publications

(4 citation statements)

References 30 publications

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“…Following this idea, several studies approached the problem from a lead time minimization and strategic decision-making perspective. These addressed the problem of possible congestion at chargers and developed approaches that optimize charger locations and delivery routes simultaneously ( 21 – 24 ). To overcome the delivery range limitation, researchers also proposed the use of public transport systems to support drones on their delivery routes ( 3 , 4 , 25 , 26 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Cokyasar

Stinson

Sahin

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. In this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

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Section: Literature Reviewmentioning

confidence: 99%

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Cokyasar

Stinson

Sahin

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Saleu et al (2022) modifies the parallel drone scheduling traveling salesman problem by taking into consideration multiple vehicles, with deliveries divided between a vehicle and one or more drones. Cokyasar et al (2021) work offers a drone delivery network concept that uses automated battery-swapping machines to increase the travel range of drones. Their problem is similar to ours in that the tanker refills the sprayers' tanks in order for the sprayers to continue their praying operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Matheuristic for Synchronized Vehicle Routing Problem with Multiple Constraints and Variable Service Time

Alkaabneh

Bonku

2023

Preprint

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“…In a case study of Seattle, U.S., Shavarani et al (2018) proposed a heuristic solution to determine an optimal arrangement of MFC locations and supporting drone recharge stations to completely replace Amazon's current van delivery system. Cokyasar at al. (2021) also developed and applied a heuristic programming method in a Chicago case study; the method located battery-swapping stations and allocated discrete demand points to the stations and allowed for a parallel truck and drone system, where some areas of the city were served by trucks and other areas were served by drones to achieve a low cost systemwe also investigate such parallel systems in our case study.…”

Section: Drone Delivery Servicementioning

confidence: 99%

Planning delivery-by-drone micro-fulfilment centres

Lamb

Wirasinghe

Waters

2022

Transportmetrica A: Transport Science

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Delivery drones are a disruptive technology that is spurring logistics system change, such as the adoption of urban micro-fulfilment centres (MFCs). In this paper, we develop and implement a two-stage continuum approximation (CA) model of this disruptive system in a geographic information system (GIS). The model includes common CA techniques at a local level to minimise cost, and then these local solutions are used in a second stage regional location-allocation multiple knapsack problem. We then compare the drone MFC system to a traditional delivery-by-truck system and investigate potential cost or emissions savings by adjusting time-window demand, logistical sprawl, electric truck alternatives, and MFC emissions. Furthermore, we conduct a sensitivity analysis to show that uncertainty in demand and effective storage density both significantly influence the number of MFCs selected and benchmark our model against commercial solvers. This methodology may also be further developed and applied to other new delivery vehicle modes.

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Designing a drone delivery network with automated battery swapping machines

Cited by 29 publications

References 30 publications

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Matheuristic for Synchronized Vehicle Routing Problem with Multiple Constraints and Variable Service Time

Planning delivery-by-drone micro-fulfilment centres

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