A Novel Framework for Solving the Optimal Path Problem in Collaborative Consignment Delivery Systems Using Drones

Drone based consignment delivery system is considered to be the future of retail marketing where unmanned aerial vehicles are employed for door delivery of items. This results in a fast, efficient and accurate delivery system which is not affected by geographic conditions and can be used for deliver...

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Vydané v:	International journal of ITS research Ročník 21; číslo 2; s. 259 - 276
Hlavní autori:	K. B., Shibu Kumar, Samuel, Philip
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2023 Springer Nature B.V
Predmet:	Algorithms Assignment problem Automotive Engineering Centroids Civil Engineering Clustering Collaboration Computer Imaging Customers Drones Electric vehicles Electrical Engineering Engineering Flight Heuristic Integer programming Optimization Particle physics Pattern Recognition and Graphics Robotics and Automation Unmanned aerial vehicles Unsupervised learning User Interfaces and Human Computer Interaction Vision Centroid based celestial clustering Drone based delivery system Travelling salesperson problem Optimal path problem Pso clustering
ISSN:	1348-8503, 1868-8659
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Abstract	Drone based consignment delivery system is considered to be the future of retail marketing where unmanned aerial vehicles are employed for door delivery of items. This results in a fast, efficient and accurate delivery system which is not affected by geographic conditions and can be used for delivery of items to areas where human reachability is difficult. Also, it can ensure that the customers avoid close contact with strangers, especially in the era of a pandemic like COVID 19. However, the system poses a number of challenges too, the most important of them being the limited battery life of drones. Though newer batteries provide better flight times, the battery discharges faster with increased number of landings and take-offs. Hence the flight of the drone has to be kept optimal for efficient delivery of consignments. In this paper, we propose a delivery system using drones where both the delivery agency and the customers collaborate together to distribute the consignments and model a novel framework for finding the optimal path of delivery drones. We employ unsupervised learning approaches to find the landing locations for drones, based on the concentration of customers. We use a nature inspired optimization algorithm using the fundamental principles of particle physics to get rid of the practical limitations of k-means, a widely used centroid based clustering technique. Besides, we address the case where the customers are spread in such a way that their partitions are not well-separated. Finally, we model, experiment and evaluate our methods on two different datasets.
AbstractList	Drone based consignment delivery system is considered to be the future of retail marketing where unmanned aerial vehicles are employed for door delivery of items. This results in a fast, efficient and accurate delivery system which is not affected by geographic conditions and can be used for delivery of items to areas where human reachability is difficult. Also, it can ensure that the customers avoid close contact with strangers, especially in the era of a pandemic like COVID 19. However, the system poses a number of challenges too, the most important of them being the limited battery life of drones. Though newer batteries provide better flight times, the battery discharges faster with increased number of landings and take-offs. Hence the flight of the drone has to be kept optimal for efficient delivery of consignments. In this paper, we propose a delivery system using drones where both the delivery agency and the customers collaborate together to distribute the consignments and model a novel framework for finding the optimal path of delivery drones. We employ unsupervised learning approaches to find the landing locations for drones, based on the concentration of customers. We use a nature inspired optimization algorithm using the fundamental principles of particle physics to get rid of the practical limitations of k-means, a widely used centroid based clustering technique. Besides, we address the case where the customers are spread in such a way that their partitions are not well-separated. Finally, we model, experiment and evaluate our methods on two different datasets.
Author	Samuel, Philip K. B., Shibu Kumar
Author_xml	– sequence: 1 givenname: Shibu Kumar orcidid: 0000-0002-3783-0599 surname: K. B. fullname: K. B., Shibu Kumar email: shibukumar@rit.ac.in organization: Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Department of Computer Science and Engineering, College of Engineering Trivandrum – sequence: 2 givenname: Philip surname: Samuel fullname: Samuel, Philip organization: Department of Computer Science, Cochin University of Science and Technology
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Keywords	Centroid based celestial clustering Drone based delivery system Travelling salesperson problem Optimal path problem Pso clustering
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Snippet	Drone based consignment delivery system is considered to be the future of retail marketing where unmanned aerial vehicles are employed for door delivery of...
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SubjectTerms	Algorithms Assignment problem Automotive Engineering Centroids Civil Engineering Clustering Collaboration Computer Imaging Customers Drones Electric vehicles Electrical Engineering Engineering Flight Heuristic Integer programming Optimization Particle physics Pattern Recognition and Graphics Robotics and Automation Unmanned aerial vehicles Unsupervised learning User Interfaces and Human Computer Interaction Vision
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Title	A Novel Framework for Solving the Optimal Path Problem in Collaborative Consignment Delivery Systems Using Drones
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