Vehicle routing in drone-based package delivery services

Agárdi, Anita; Kovács, László; Banyai, T.

doi:10.1201/9780367824037-20

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…In the variant of the TSP-D proposed by Agárdi et al (2020), the drone must be launched and retrieved at the same node. Hence, the truck may wait for the drone.…”

Section: The Fstsp and Its Variantsmentioning

confidence: 99%

Drone-aided routing: A literature review

Macrina

Pugliese

Guerriero

et al. 2020

Transportation Research Part C: Emerging Technologies

307

109

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“…In the variant of the TSP-D proposed by Agárdi et al (2020), the drone must be launched and retrieved at the same node. Hence, the truck may wait for the drone.…”

Section: The Fstsp and Its Variantsmentioning

confidence: 99%

Drone-aided routing: A literature review

Macrina

Pugliese

Guerriero

et al. 2020

Transportation Research Part C: Emerging Technologies

307

109

View full text Add to dashboard Cite

“…Execution decisions about the transportation process are observed mostly in vehicle routing problems. Vehicle routing is used to optimize the distribution path economically (Müller, Jaeger and Hanewinkel 2019;van Lopik et al 2020;Agárdi, Kovács and Bányai 2019) and environmentally (Ranieri et al 2018;Su and Fan 2020), or to re-route products whose quality was impaired during delivery (Sciortino et al 2016;Ben-Daya, Hassini and Bahroun 2019). Among the planning decisions impacted by the adoption of Logistics 4.0, order rescheduling decisions are cited most frequently (Bányai 2018;Bányai, Illés and Bányai 2018), together with asset repositioning (Daudi and Thoben 2020).…”

Section: Level Of Automation Frequency Of Acquisition Level Of Autonomymentioning

confidence: 99%

“…The outcome of the present study also opens a future research opportunity, that of measuring the impact that each Logistics 4.0 principle has on overall transportation process performance. This impact could be measured, for example, by quantifying the reduction in costs that autonomous vehicles can bring to the last mile delivery process (Agárdi, Kovács and Bányai 2019) or by quantifying the decrease in collaboration costs ensuing from bringing together the different actors involved in the transportation process. It could also be possible to measure the impact that the combination of the different process layers has on a specific transportation process performance.…”

Section: Limitations and Future Directions Of Researchmentioning

confidence: 99%

Empowering freight transportation through Logistics 4.0: a maturity model for value creation

Modica

Colicchia

Tappia

2021

Production Planning & Control

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Logistics 4.0 is a recognized lever for improving efficiency, effectiveness and sustainability in logistics processes. Of these, the increasingly complex area of transport plays a key role in companies, as it directly impacts costs and service level. This paper investigates how Logistics 4.0 affects the design and configuration of the transportation process, so that companies can exploit the related benefits and create value. Using a systematic literature review, this study provides a framework for Logistics 4.0 in transportation, which includes the process dimensions to be taken into account when designing such a process. A maturity model complements the framework, which presents the configuration options for each process dimension in an evolutionary path from low to high implementation level. This paper is original in that it takes a novel process perspective, crucial for supporting the implementation of Logistics 4.0 in transportation, and discusses the associated opportunities for companies.

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“…Drone-based material handling is becoming increasingly important today, as advances in technology and increasingly sophisticated regulation allow drone transport to be used in more and more areas of practice. The deployment of drone systems requires a number of planning tasks that require rethinking logistics tasks such as route planning, scheduling, and resource allocation [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Gubán

Udvaros

2022

Drones

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In this paper, a mathematical model and solution for performing the inventory tasks of a multi-user, mixed warehouse in which neither satellite positioning nor other IT solutions can be used was presented. After reviewing the literature on road planning and the use of drones in warehouses, a method is presented that can be used to control drones that can be moved in all directions for imaging and transmission. The proposed method consists of three main steps. As a first step, we provide the mathematical model and solution method needed to determine the (optimal execution time) access routes required for processing the compartments of the warehouses. This is an initial step before starting the inventory. This considers the structure of the warehouse, its features, the number of drones, and the parameters of the drones. In the second step, based on the routes obtained in the first step, the real-time movement of the drones was controlled during processing, including camera movement and image recording. The third step is post-processing, i.e., processing the images for QR code identification, interpreting the QR code, and recognizing empty compartments for inventory control. A major advantage for users of the solution method is that the result can be achieved automatically without an external orientation device, relying solely on its own movement and the organization of a pre-planned route. The proposed model and solution method are suitable not only for inventory control, but also for solving other problems matching the model.

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Vehicle routing in drone-based package delivery services

Cited by 7 publications

References 1 publication

Drone-aided routing: A literature review

Drone-aided routing: A literature review

Empowering freight transportation through Logistics 4.0: a maturity model for value creation

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

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