Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations

Moadab, Amirhossein; Farajzadeh, Fatemeh; Valilai, Omid Fatahi

doi:10.1038/s41598-022-10408-4

Cited by 35 publications

(11 citation statements)

References 48 publications

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“…From an Environmental aspect , utilizing vehicles with high carbon footprint causes an international crisis of global warming. Though it’s unavoidable to use airplanes for transportation of products from long-distance locations, truck delivery and especially for products that are not large in terms of their size can be alternated with a green vehicle like robots and drones (Farajzadeh et al, 2020; Moadab et al, 2022 , Song et al, 2022 ). The environmental cost is not negligible, and the harmful effects of carbon emission of the operation can cause more damage to the economy and involve more people.…”

Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

Self Cite

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“…al. proposed using public transportation vehicles as charging stations for drones, where the drones can get recharged on public transportation vehicles, or ride on them to conserve energy [25]. Huang et.…”

Section: Mobile-unit Charging Strategiesmentioning

confidence: 99%

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

Huang,

Li,

Chen

et al. 2024

Preprint

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Recently, Unmanned Aerial Vehicles (UAVs) have been proposed as the carriers of mobile edge servers. Such UAV-mounted edge servers can offer computation offloading service to users more efficiently due to their fast movement. However, the limited battery endurance of the UAVs places a real challenge. In this paper, we propose MobiPower: a two-layer UAV-based edge computing and charging scheme for Internet of Vehicles (IoVs). The UAV-mounted edge servers fly in the air to serve the IoV nodes, while the Mobile Charging Stations (MCSs) move on ground to charge these UAVs. As the mobile charging stations are low-speed trucks which are difficult to respond to the realtime traffic change, we predict the future traffic based on the historical data to facilitate obtaining the offline scheduling strategies in advance. Moreover, we use hierarchical clustering to analyze the traffic patterns and classify the regions with same pattern into the same cluster. Mobile charging stations will only be scheduled between different clusters to reduce the complexity. Finally, we design an auction-based mobile charging station scheduling algorithm to maximize the amount of offloaded tasks and reduce the moving distance of the mobile charging stations. We perform the analysis and experiments on a popular trace, i.e., the TaxiBJ trace, and the experimental results show that our heuristic strategies effectively schedule the mobile charging stations over the entire map and demonstrate superior performance compared with existing popular methods, e.g., up to 36.46% improvement in terms of task offloading ratio and 39.06% less scheduling distance of the mobile charging stations under typical scenarios.

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“…In the end, the UAVs could return to the vehicles to be recharged. This cooperation between vehicles and UAVs could be extended to utilize public transportation (buses), raising synchronization issues in such problems [73].…”

Section: Energy Consumption In Last-mile Deliverymentioning

confidence: 99%

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Ghorbani

Herrera

Ammouriova

et al. 2022

Future Transportation

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Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.

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Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations

Cited by 35 publications

References 48 publications

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

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