Logistics Support Approach for Drone Delivery Fleet

Troudi, Asma; Addouche, Sid-Ali; Dellagi, Sofiène; Mhamedi, Abderrahman El

doi:10.1007/978-3-319-59513-9_9

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…To highlight the improvement of Unmanned Aircraft Vehicles in civil use, Troudi et al. [36] proposed a Post‐Production Logistics Support Analysis to cover the exploitation phase of a drone‐delivery operator. Iranmanesh and Raad [37] proposed a novel method to data communications by providing a data delivery service along with the drone parcel delivery service.…”

Section: Literature Surveymentioning

confidence: 99%

“…Raj and Sah [35] considered better infrastructure as a critical success factor for the adoption of drones to find vertiports and service centres in prime locations for drone supporting infrastructure. To highlight the improvement of Unmanned Aircraft Vehicles in civil use, Troudi et al [36] proposed a Post-Production Logistics Support Analysis to cover the exploitation phase of a drone-delivery operator. Iranmanesh and Raad [37] proposed a novel method to data communications by providing a data delivery service along with the drone parcel delivery service.…”

Section: Stand-alone Drone Logistics Systemmentioning

confidence: 99%

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RETRACTED: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India

Gabani

Gala

Narwane

et al. 2021

IET Collab Intel Manufact

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Emerging Unmanned Aerial Vehicle (UAV) technologies have been under spotlight worldwide due to a promising spectrum of applications. Despite interesting use-cases receiving worldwide media attention, drones are expected to have their most significant impact on supply chain and logistics systems in highly populated urban cities once the regulations for commercial purposes are lifted. This article proposes two models of drone-delivery technology that can serve as a potential replacement in the Indian truckdelivery system. First, a drone-truck hybrid delivery model is proposed with trucks carrying multiple drones. Second, a stand-alone drone-delivery model is proposed with separate charging infrastructural facilities at specific points. The two proposed models' findings present the advantages of significantly reducing carbon emissions, delivery costs, and time compared to truck-based delivery. The hybrid drone-truck model eliminates the need for separate charging facilities saving initial infrastructural costs. In contrast, the stand-alone drone model utilizes the current warehouse location facilities with charging stations on specific points for ease of adoption. This research presents initial viability suggesting that the drone technology can solve the current operational challenges in India's urban cities. Furthermore, vital factors are discussed to enable stakeholders to select a model for drone logistics systems planning and experimentations.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Literature Surveymentioning

confidence: 99%

Section: Stand-alone Drone Logistics Systemmentioning

confidence: 99%

RETRACTED: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India

Gabani

Gala

Narwane

et al. 2021

IET Collab Intel Manufact

View full text Add to dashboard Cite

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“…Amazon [31] and Google [32] have trial projects for drone delivery systems. Moormann et al [33] considers a medication delivery application.…”

Section: Drone Routing Applicationsmentioning

confidence: 99%

A learnheuristic approach for the team orienteering problem with aerial drone motion constraints

Bayliss

Juan

Currie

et al. 2020

Applied Soft Computing

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This work proposes a learnheuristic approach (combination of heuristics with machine learning) to solve an aerial-drone team orienteering problem. The goal is to maximise the total reward collected from information gathering or surveillance observations of a set of known targets within a fixed amount of time. The aerial drone team orienteering problem has the complicating feature that the travel times between targets depend on a drone's flight path between previous targets. This path-dependence is caused by the aerial surveillance drones flying under the influence of air-resistance, gravity, and the laws of motion. Sharp turns slow drones down and the angle of ascent and air-resistance influence the acceleration a drone is capable of. The route dependence of intertarget travel times motivates the consideration of a learnheuristic approach, in which the prediction of travel times is outsourced to a machine learning algorithm. This work proposes an instance-based learning algorithm with interpolated predictions as the learning module. We show that a learnheuristic approach can lead to higher quality solutions in a shorter amount of time than those generated from an equivalent metaheuristic algorithm, an effect attributed to the search-diversity enhancing consequence of the online learning process.

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“…This paper, which is based on the work of Troudi et al [35], presents a configuration of a drone operator that delivers parcels with drones. In this context, we are describing the delivery process of the drone delivery activities.…”

Section: Motivation and Target Contributionmentioning

confidence: 99%

“…When a UAV starts its mission, it is considered as an active vehicle. Through the logistics analysis approach discussed in [35], the authors focused on the importance of analyzing the activity in order to support the evolution of the fleet with an appropriate logistics support system. The approach is based on the standard of logistics support analysis MIL-STD-1388-1A [36] and the extension of the same tasks in this analysis in the post-production phase.…”

Section: Motivation and Target Contributionmentioning

confidence: 99%

Sizing of the Drone Delivery Fleet Considering Energy Autonomy

et al. 2018

Self Cite

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One of the most innovative solutions treated in the literature in order to reduce the environmental impact of urban parcel delivery logistics is the use of drones for delivery on the last kilometer. Consequently, nowadays, the primary challenge is essentially related to the drones’ fleet sizing according to its means of support for the urban delivery of parcels. In this paper, we will discuss the issue of dimensioning from a forecast of deliveries of an urban perimeter, the size of the fleet, the stock of battery to dispose of and the strategy of battery charging. We will present an analytical model expressing the proposed problem of the optimal drones’ delivery mission taking into account the issues of autonomy and energy consumption related to the drone’s technical specification. According to the developed analytical model, two optimization policies will be proposed. The first policy consists of planning missions under reducing distance. The second policy tries to make a compromise between the distance and the number of drones. A case study will be presented in order to compare the two policies based on the overall cost of a plan. The main objective of the study is to create a decision-making tool for the design of a drone fleet in the case of forecast deliveries over a time horizon under operational constraints.

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Logistics Support Approach for Drone Delivery Fleet

Cited by 12 publications

References 11 publications

RETRACTED: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India

RETRACTED: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India

A learnheuristic approach for the team orienteering problem with aerial drone motion constraints

Sizing of the Drone Delivery Fleet Considering Energy Autonomy

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