A range-restricted recharging station coverage model for drone delivery service planning

Hong, Insu; Kuby, Michael; Murray, Alan T.

doi:10.1016/j.trc.2018.02.017

Cited by 176 publications

(100 citation statements)

References 44 publications

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“…The model assumes that the drone first rises vertically to the altitude of the largest obstacle z o along its path (plus 5 m safety distance). Then it travels horizontally following the direct line between the base station and the patient's site, which is described by the Euclidean distance between (x b , y b ) and (x p , y p ) [34,30,50]. Finally, the drone descends vertically to the patient's site.…”

Section: Definition Descriptionmentioning

confidence: 99%

Optimal allocation of defibrillator drones in mountainous regions

et al. 2020

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Responding to emergencies in Alpine terrain is quite challenging as air ambulances and mountain rescue services are often confronted with logistics challenges and adverse weather conditions that extend the response times required to provide life-saving support. Among other medical emergencies, sudden cardiac arrest (SCA) is the most time-sensitive event that requires the quick provision of medical treatment including cardiopulmonary resuscitation and electric shocks by automated external defibrillators (AED). An emerging technology called unmanned aerial vehicles (or drones) is regarded to support mountain rescuers in overcoming the time criticality of these emergencies by reducing the time span between SCA and early defibrillation. A drone that is equipped with a portable AED can fly from a base station to the patient's site where a bystander receives it and starts treatment. This paper considers such a response system and proposes an integer linear program to determine the optimal allocation of drone base stations in a given geographical region. In detail, the developed model follows the objectives to minimize the number of used drones and to minimize the average travel times of defibrillator drones responding to SCA patients. In an example of application, under consideration of historical helicopter response times, the authors test the developed model and demonstrate the capability of drones to speed up the delivery of AEDs to SCA patients. Results indicate that time spans between SCA and early defibrillation can be reduced by the optimal allocation of drone base stations in a given geographical region, thus increasing the survival rate of SCA patients.

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Section: Definition Descriptionmentioning

confidence: 99%

Optimal allocation of defibrillator drones in mountainous regions

et al. 2020

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“…Scott and Scott [8] studied some innovative applications of drones in healthcare, and they proposed two models to locate the central depot of drone deliveries. Hong et al [2] considered the limited flight distance of drones that are powered by batteries or fuel, and they investigated the design of station locations and the delivery routes. A mixed-integer linear programming formulation and a heuristic were developed.…”

Section: Tactical-level Drone Facility Location and Drone Fleet Deplomentioning

confidence: 99%

“…Existing studies on drone delivery optimization problems usually focus on tactical-level drone facility location problems (Scott and Scott [8]; Hong et al [2]; Shavarani et al [9]), and operational-level drone routing problems (Murray and Chu [10]; Chow [11]; Tavana et al [12]; Dorling et al [13]). In the drone delivery systems of JD.com and Ele.me, (drone) service routes are designated and fixed and the optimization of drone fleet deployment and planning (DFDP) decisions can greatly improve the delivery efficiency.…”

mentioning

confidence: 99%

Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service

Liu

Zhu

et al. 2019

Sustainability

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Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches.

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“…Following the second model, some publications have considered the facility location problem under the context of drone delivery to extend the delivery area [23,24]. Clearly, one disadvantage of this method is the investment in constructing the new facilities.…”

Section: Introductionmentioning

confidence: 99%

“…The current paper also focuses on addressing this shortcoming. Unlike [23,24] to place more facilities, we introduce the idea of drone-vehicle collaboration [25,26]. Here, the vehicle refers to the public transportation vehicles.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of a Parcel Delivery System Consisting of an Aerial Drone Interacting with Public Transportation Vehicles

Huang

Savkin

Huang

2020

Sensors

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This paper proposes a novel parcel delivery system which consists of a drone and public transportation vehicles such as trains, trams, etc. This system involves two delivery schemes: drone-direct scheme referring to delivering to a customer by a drone directly and drone–vehicle collaborating scheme referring to delivering a customer based on the collaboration of a drone and public transportation vehicles. The fundamental characteristics including the delivery time, energy consumption and battery recharging are modelled, based on which a time-dependent scheduling problem for a single drone is formulated. It is shown to be NP-complete and a dynamic programming-based exact algorithm is presented. Since its computational complexity is exponential with respect to the number of customers, a sub-optimal algorithm is further developed. This algorithm accounts the time for delivery and recharging, and it first schedules the customer which leads to the earliest return. Its computational complexity is also discussed. Moreover, extensive computer simulations are conducted to demonstrate the scheduling performance of the proposed algorithms and the impacts of several key system parameters are investigated.

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A range-restricted recharging station coverage model for drone delivery service planning

Cited by 176 publications

References 44 publications

Optimal allocation of defibrillator drones in mountainous regions

Optimal allocation of defibrillator drones in mountainous regions

Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service

Scheduling of a Parcel Delivery System Consisting of an Aerial Drone Interacting with Public Transportation Vehicles

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