Zoning a Service Area of Unmanned Aerial Vehicles for Package Delivery Services

Sung, Inkyung; Nielsen, Peter Axel

doi:10.1007/s10846-019-01045-7

Cited by 38 publications

(28 citation statements)

References 27 publications

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“…Considering a competitive environment, where different online retailers compete with each other, Baloch and Gzara (2020) provide a nonlinear mathematical model as well as logic-based benders decomposition approach to decide on which facilities should be opened, so that the overall profitability of the retailer is maximized. While the majority of the literature ignores limitations of drones sharing the same airspace, safety issues are addressed by Sung and Nielsen (2020). They propose a genetic algorithm which decomposes the service area into several disjoint zones each operated by a single drone.…”

Section: Dronesmentioning

confidence: 99%

Last-mile delivery concepts: a survey from an operational research perspective

2020

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In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

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Section: Dronesmentioning

confidence: 99%

Last-mile delivery concepts: a survey from an operational research perspective

2020

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“…A critical step toward the challenge is achieving an intelligent segmentation of an area of interest which can be covered by multiple vehicles; this segmentation is somewhat achieved by convex decomposition. Based on existing studies on service area segmentation [18], the convex decomposition method in this paper deserves further extension, with a focus on how critical the segmentation is, in terms of the quality of service at both fleet and individual vehicle levels.…”

Section: Convex Decomposition Under Multi-unit Operational Environmentioning

confidence: 99%

Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges

Nielsen

Sung

Nielsen

2019

Sensors

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To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples.

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“…However, a solution gained by the method used in Reference [ 16 ] might not minimize cost. In Reference [ 17 ], Sung et al also assign UAVs to specific zones that are divided based on deep learning algorithm previously, but the cost might be not minimum. In Reference [ 5 ], total delivery costs using only truck, only drone, and both truck and drone were compared.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

Kim

Kwak

et al. 2021

Sensors

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A delivery service using unmanned aerial vehicles (UAVs) has potential as a future business opportunity, due to its speed, safety and low-environmental impact. To operate a UAV delivery network, a management system is required to optimize UAV delivery routes. Therefore, we create a routing algorithm to find optimal round-trip routes for UAVs, which deliver goods from depots to customers. Optimal routes per UAV are determined by minimizing delivery distances considering the maximum range and loading capacity of the UAV. In order to accomplish this, we propose an algorithm with four steps. First, we build a virtual network to describe the realistic environment that UAVs would encounter during operation. Second, we determine the optimal number of in-service UAVs per depot. Third, we eliminate subtours, which are infeasible routes, using flow variables part of the constraints. Fourth, we allocate UAVs to customers minimizing delivery distances from depots to customers. In this process, we allow multiple UAVs to deliver goods to one customer at the same time. Finally, we verify that our algorithm can determine the number of UAVs in service per depot, round-trip routes for UAVs, and allocate UAVs to customers to deliver at the minimum cost.

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Zoning a Service Area of Unmanned Aerial Vehicles for Package Delivery Services

Cited by 38 publications

References 27 publications

Last-mile delivery concepts: a survey from an operational research perspective

Last-mile delivery concepts: a survey from an operational research perspective

Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges

An Optimal Routing Algorithm for Unmanned Aerial Vehicles

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