Journal articles: 'Vehicle routing problem with drone delivery'

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Ito, Satoshi, Keishi Akaiwa, Yusuke Funabashi, Hiroki Nishikawa, Xiangbo Kong, Ittetsu Taniguchi, and Hiroyuki Tomiyama. "Load and Wind Aware Routing of Delivery Drones." Drones 6, no. 2 (February 17, 2022): 50. http://dx.doi.org/10.3390/drones6020050.

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Delivery drones have been attracting attention as one of the promising technologies to deliver packages. Several research studies on routing problems specifically for drone delivery scenarios have extended Vehicle Routing Problems (VRPs). Most existing VRPs are based on Traveling Salesman Problems (TSPs) for minimizing the overall distance. On the other hand, VRPs for drone delivery have been aware of energy consumption due to the consideration of battery capacity. Despite hovering motions with loading packages accounting for a large portion of energy consumption since delivery drones need to hover with several packages, little research has been conducted on drone routing problems that aim at the minimization of overall flight times. In addition, flight time is strongly influenced by windy conditions such as headwinds and tailwinds. In this paper, we propose a VRP for drone delivery in which flight time is dependent on the weight of packages in a windy environment, called Flight Speed-aware Vehicle Routing Problem with Load and Wind (FSVRPLW). In this paper, flight speed changes depending on the load and wind. Specifically, a heavier package slows down flight speeds and a lighter package speeds up flight speeds. In addition, a headwind slows down flight speeds and a tailwind speed up flight speeds. We mathematically derived the problem and developed a dynamic programming algorithm to solve the problem. In the experiments, we investigate how much impact both the weight of packages and the wind have on the flight time. The experimental results indicate that taking loads and wind into account is very effective in reducing flight times. Moreover, the results of comparing the effects of load and wind indicate that flight time largely depends on the weight of packages.

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Prawira, Hasan Aji, and Budi Santosa. "Development of Particle Swarm Optimization and Simulated Annealing Algorithms to Solve Vehicle Routing Problems with Drones." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 5, no. 1 (July 6, 2021): 1–11. http://dx.doi.org/10.21070/prozima.v5i1.1398.

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Vehicle Routing Problem with Drone (VRPD) is a problem of determining the number of routes for delivery of goods from the depot to a number of customers using trucks and drones. Drones are an alternative delivery tool besides trucks, each truck can be equipped with a support drone. Drones can be used to make a delivery while the truck is making others. By combining a truck and a drone, the truck can act as a tool for drone launch and landing so that the drones can reach long distances from the depot. The purpose of this problem is to minimize the cost of sending goods by trucks and drones. In this study, the Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) are proposed to solve these problems. The Route Drone algorithm are used to help change the structure of the PSO and SA solutions into a VRPD solution. The proposed algorithm has been applied to 24 different scenarios ranging from 6 customers to 100 customers. The PSO and SA algorithms are able to find solutions that are close to optimal. The SA is able to find a better solution than the PSO.

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Dorling, Kevin, Jordan Heinrichs, Geoffrey G. Messier, and Sebastian Magierowski. "Vehicle Routing Problems for Drone Delivery." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 1 (January 2017): 70–85. http://dx.doi.org/10.1109/tsmc.2016.2582745.

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Kim, Seongheon, and Suhwan Kim. "VRP of Drones Considering Power Consumption Rate and Wind Effects." LOGI – Scientific Journal on Transport and Logistics 13, no. 1 (January 1, 2022): 210–21. http://dx.doi.org/10.2478/logi-2022-0019.

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Abstract The drone industry is one of the most important areas of the Fourth Industrial Revolution. In the drone industry, delivery systems using drones are now facing commercialization as they have undergone many experiments and discussions. The purpose of this study is to find the best route in a delivery system using a drone. In this study, we have developed the existing Vehicle Routing Problem (VRP) into a more realistic mathematical model considering the two differences between drones and vehicles; one is that power consumption varies with the weight of the loaded cargo and the other is that velocity is influenced by wind. This study also presents an Ant Colony System (ACS) algorithm to effectively solve VRP, a well-known NP-hard problem. The methodology of this study is quite successful and is expected to enable more realistic and effective routing decisions.

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Sitek, Paweł, Jarosław Wikarek, and Mieczysław Jagodziński. "A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)." Applied Sciences 12, no. 16 (August 18, 2022): 8255. http://dx.doi.org/10.3390/app12168255.

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Unmanned aerial vehicles (UAVs), also known as drones, are increasingly common and popular due to their relatively low prices and high mobility. The number of areas for their practical applications is rapidly growing. The most promising are: last-mile delivery, emergency response, the inspection of technical devices and installations, etc. In these applications, it is often necessary to solve vehicle routing problems, formulated as a variant of the vehicle routing problems with drones (VRPD). This study presents a proactive approach to a modified and extended VRPD, including: the dynamic selection of drone take-off points, bidirectional delivery (delivery and pick up), various types of shipments, allocation of shipments to drones and drones to vehicles, the selection of the optimal number of drones, etc. Moreover, a formal model of constraints and questions for the extended vehicle routing problem with drones (EVRPD) and exact and approximate methods for solving it have been proposed. The proposed model can be the basis for supporting proactive and reactive decisions regarding last-mile delivery, particularly the selection of the necessary fleet, starting points, the identification of specific shipments that prevent delivery with available resources, etc. The study also includes the results of numerous computational experiments verifying the effectiveness of the implementation methods. The time to obtain a solution is at least 20 times shorter for the proposed DGA (dedicated genetic algorithm) than for the mathematical programming solvers such as Gurobi or LINGO. Moreover, for larger-sized data instances, these solvers do not allow obtaining any solution in an acceptable time, or they obtain worse solutions.

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Karak, Aline, and Khaled Abdelghany. "The hybrid vehicle-drone routing problem for pick-up and delivery services." Transportation Research Part C: Emerging Technologies 102 (May 2019): 427–49. http://dx.doi.org/10.1016/j.trc.2019.03.021.

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Afzal, Kiran, Rehan Tariq, Farhan Aadil, Zeshan Iqbal, Nouman Ali, and Muhammad Sajid. "An Optimized and Efficient Routing Protocol Application for IoV." Mathematical Problems in Engineering 2021 (May 19, 2021): 1–32. http://dx.doi.org/10.1155/2021/9977252.

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IoV is the latest application of VANET and is the alliance of Internet and IoT. With the rapid progress in technology, people are searching for a traffic environment where they would have maximum collaboration with their surroundings which comprise other vehicles. It has become a necessity to find such a traffic environment where we have less traffic congestion, minimum chances of a vehicular collision, minimum communication delay, fewer communication errors, and a greater message delivery ratio. For this purpose, a vehicular ad hoc network (VANET) was devised where vehicles were communicating with each other in an infrastructureless environment. In VANET, vehicles communicate in an ad hoc manner and communicate with each other to deliver messages, for infotainment purposes or for warning other vehicles about emergency scenarios. Unmanned aerial vehicle- (UAV-) assisted VANET is one of the emerging fields nowadays. For VANET’s routing efficiency, several routing protocols are being used like optimized link state routing (OLSR) protocol, ad hoc on-demand distance vector (AODV) routing protocol, and destination-sequenced distance vector (DSDV) protocol. To meet the need of the upcoming era of artificial intelligence, researchers are working to improve the route optimization problems in VANETs by employing UAVs. The proposed system is based on a model of VANET involving interaction with aerial nodes (UAVs) for efficient data delivery and better performance. Comparisons of traditional routing protocols with UAV-based protocols have been made in the scenario of vehicle-to-vehicle (V2V) communication. Later on, communication of vehicles via aerial nodes has been studied for the same purpose. The results have been generated through various simulations. After performing extensive simulations by varying different parameters over grid sizes of 300 × 1500 m to 300 × 6000 m, it is evident that although the traditional DSDV routing protocol performs 14% better than drone-assisted destination-sequenced distance vector (DA-DSDV) when we have number of sinks equal to 25, the performance of drone-assisted optimized link state routing (DA-OLSR) protocol is 0.5% better than that of traditional OLSR, whereas drone-assisted ad hoc on-demand distance vector (DA-AODV) performs 22% better than traditional AODV. Moreover, if we increase the number of sinks up to 50, it can be clearly seen that the DA-AODV outperforms the rest of the routing protocols by up to 60% (either traditional routing protocol or drone-assisted routing protocol). In addition, for parameters like MAC/PHY overhead and packet delivery ratio, the performance of our proposed drone-assisted variants of protocols is also better than that of the traditional routing protocols. These results show that our proposed strategy performs better than the traditional VANET protocols and plays important role in minimizing the MAC/PHY and enhancing the average throughput along with average packet delivery ratio.

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Norba, Iryna. "Vehicle Routing Problem When Using UAVs." Cybernetics and Computer Technologies, no. 4 (December 30, 2021): 27–34. http://dx.doi.org/10.34229/2707-451x.21.4.3.

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Introduction. In recent years, the use of unmanned aerial vehicles (UAVs) is growing rapidly. Initially introduced for military purposes, drones and related technologies have been successfully switched to a number of new civilian applications in the last few years, such as delivery, logistics, surveillance, entertainment, and more. They also opened up new opportunities, such as working in difficult or dangerous areas. The UAV has the potential to solve the problem of air mobility, allowing to change transport and logistics in the future. Combining UAVs with traditional land vehicles can solve the last-mile delivery problem by achieving significant improvements in distribution costs and speed of vehicle delivery. One of the biggest challenges is to plan UAV routes with a number of constraints, including time, distance or energy costs, cargo weight, environmental and environmental conditions (such as wind direction or obstacles), UAV battery life, and demand requirements. users you want to visit. Thus, it revealed the need to classify different types of research and study the general characteristics of the study area. This article aims to help identify the main topics and new areas of research, as well as provides a published overview of the current state and contribution to the problem of UAV routing, as well as a general categorization of the problem of vehicle routing (VRP). The purpose of the paper is to analyze the scientific contributions to the problem of UAV routing to determine the main characteristics of these problems, as well as trends in research and recent improvements. Results. Sources are classified according to the areas of application of UAVs; methods that include exact, heuristic, metaheuristic, and mixed algorithms are mentioned. Conclusions. An overview of the work on routing problems using UAVs and the tasks they generate, trends in research and recent developments. Keywords: Unmanned aerial vehicle, routing, vehicle, optimization.

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Choudhury, Shushman, Kiril Solovey, Mykel J. Kochenderfer, and Marco Pavone. "Efficient Large-Scale Multi-Drone Delivery using Transit Networks." Journal of Artificial Intelligence Research 70 (February 17, 2021): 757–88. http://dx.doi.org/10.1613/jair.1.12450.

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We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas. Besides flying directly, drones can use public transit vehicles such as buses and trams as temporary modes of transportation to conserve energy. Adding this capability to our formulation augments effective drone travel range and the space of possible deliveries but also increases problem input size due to the large transit networks. We present a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery and addresses the multifaceted computational challenges of our problem through a two-layer approach. First, the upper layer assigns drones to package delivery sequences with an approximately optimal polynomial time allocation algorithm. Then, the lower layer executes the allocation by periodically routing the fleet over the transit network, using efficient, bounded suboptimal multi-agent pathfinding techniques tailored to our setting. We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DC Metropolitan Area. Our framework computes solutions for most settings within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.

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Canca, David, Belén Navarro-Carmona, and José Luis Andrade-Pineda. "Design and Assessment of an Urban Circular Combined Truck–Drone Delivery System Using Continuum Approximation Models and Integer Programming." Sustainability 14, no. 20 (October 18, 2022): 13459. http://dx.doi.org/10.3390/su142013459.

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The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.

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Yang, Byounghak. "A capacitated multi-vehicle stop points location-routing-allocation problem in the drone-vehicle delivery system." Journal of the Korean Society of Supply Chain Management 19, no. 1 (May 31, 2019): 45–56. http://dx.doi.org/10.25052/kscm.2019.05.19.1.45.

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Kitjacharoenchai, Patchara, and Seokcheon Lee. "Vehicle Routing Problem with Drones for Last Mile Delivery." Procedia Manufacturing 39 (2019): 314–24. http://dx.doi.org/10.1016/j.promfg.2020.01.338.

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Chung, Yerim, Taejoon Park, and Yunhong Min. "Usefulness of Drones in the Urban Delivery System: Solving the Vehicle and Drone Routing Problem with Time Window." Journal of the Korean Operations Research and Management Science Society 41, no. 3 (August 31, 2016): 75–96. http://dx.doi.org/10.7737/jkorms.2016.41.3.075.

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Du, Lijing, Xiaohuan Li, Yuan Gan, and Kaijun Leng. "Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies." Sustainability 14, no. 8 (April 13, 2022): 4651. http://dx.doi.org/10.3390/su14084651.

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To reduce distribution risk and improve the efficiency of medical materials delivery under major public health emergencies, this paper introduces a drone routing problem with time windows. A mixed-integer programming model is formulated considering contactless delivery, total travel time, and customer service time windows. Utilizing Dantzig–Wolfe decomposition, the proposed optimization model is converted into a path-based master problem and a pricing subproblem based on an elementary shortest path problem with resource constraints. We embed the pulse algorithm into a column generation framework to solve the proposed model. The effectiveness of the model and algorithm is verified by addressing different scales of Solomon datasets. A case study on COVID-19 illustrates the application of the proposed model and algorithm in practice. We also perform a sensitivity analysis on the drone capacity that may affect the total distribution time. The experimental results enrich the research related to vehicle routing problem models and algorithms under major public health emergencies and provide optimized relief distribution solutions for decision-makers of emergency logistics.

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Huang, Shan-Huen, Ying-Hua Huang, Carola A. Blazquez, and Chia-Yi Chen. "Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm." Advanced Engineering Informatics 51 (January 2022): 101536. http://dx.doi.org/10.1016/j.aei.2022.101536.

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Kitjacharoenchai, Patchara, Byung-Cheol Min, and Seokcheon Lee. "Two echelon vehicle routing problem with drones in last mile delivery." International Journal of Production Economics 225 (July 2020): 107598. http://dx.doi.org/10.1016/j.ijpe.2019.107598.

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Khoufi, Ines, Anis Laouiti, and Cedric Adjih. "A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles." Drones 3, no. 3 (August 24, 2019): 66. http://dx.doi.org/10.3390/drones3030066.

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The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, and so forth. They have opened new possibilities such as allowing operation in otherwise difficult or hazardous areas, for instance. For all applications, one foremost concern is the selection of the paths and trajectories of UAVs, and at the same time, UAVs control comes with many challenges, as they have limited energy, limited load capacity and are vulnerable to difficult weather conditions. Generally, efficiently operating a drone can be mathematically formalized as a path optimization problem under some constraints. This shares some commonalities with similar problems that have been extensively studied in the context of urban vehicles and it is only natural that the recent literature has extended the latter to fit aerial vehicle constraints. The knowledge of such problems, their formulation, the resolution methods proposed—through the variants induced specifically by UAVs features—are of interest for practitioners for any UAV application. Hence, in this study, we propose a review of existing literature devoted to such UAV path optimization problems, focusing specifically on the sub-class of problems that consider the mobility on a macroscopic scale. These are related to the two existing general classic ones—the Traveling Salesman Problem and the Vehicle Routing Problem. We analyze the recent literature that adapted the problems to the UAV context, provide an extensive classification and taxonomy of their problems and their formulation and also give a synthetic overview of the resolution techniques, performance metrics and obtained numerical results.

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Zhang, Shuai, Siliang Liu, Weibo Xu, and Wanru Wang. "A novel multi-objective optimization model for the vehicle routing problem with drone delivery and dynamic flight endurance." Computers & Industrial Engineering 173 (November 2022): 108679. http://dx.doi.org/10.1016/j.cie.2022.108679.

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Poikonen, Stefan, and Bruce Golden. "The Mothership and Drone Routing Problem." INFORMS Journal on Computing 32, no. 2 (April 2020): 249–62. http://dx.doi.org/10.1287/ijoc.2018.0879.

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The mothership and drone routing problem (MDRP) considers the routing of a two-vehicle tandem. The larger vehicle, which may be a ship or an airplane, is called the mothership; the smaller vehicle, which may be a small boat or unmanned aerial vehicle, is called the drone. We assume that there exists a set of target locations T. For each t in T, the drone must launch from the mothership, visit t, and then return to the mothership to refuel. The drone has a limited range of R time units. In the MDRP, we assume that both mothership and drone operate in the “open seas” (i.e., using the Euclidean metric). We also introduce the mothership and infinite-capacity drone routing problem (MDRP-IC), where a drone launches from the mothership and visits one or more targets consecutively before returning to the mothership. Our exact approach uses branch and bound, where each node of the branch-and-bound tree corresponds to a potential subsequence of the order of target visits. A lower bound at each node is given by solving a second-order cone program, which optimally chooses a launch point and landing point for each target in the subsequence. A set of heuristics that also uses a second-order cone program as an embedded procedure is presented. We show that our schemes are flexible to accommodate a variety of additional constraints and/or objective functions. Computational results and interesting variants of the MDRP and MDRP-IC are also presented.

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Wen, Xupeng, and Guohua Wu. "Heterogeneous multi-drone routing problem for parcel delivery." Transportation Research Part C: Emerging Technologies 141 (August 2022): 103763. http://dx.doi.org/10.1016/j.trc.2022.103763.

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Rich, Robert. "Inverting the Truck-Drone Network Problem to Find Best Case Configuration." Advances in Operations Research 2020 (January 22, 2020): 1–10. http://dx.doi.org/10.1155/2020/4053983.

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Many industries are looking for ways to economically use truck/rail/ship fitted with drone technologies to augment the “last mile” delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truck-drone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truck-drone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current best-in-class algorithms found in literature.

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Zeng, Fanruiqi, Zaiwei Chen, John-Paul Clarke, and David Goldsman. "Nested vehicle routing problem: Optimizing drone-truck surveillance operations." Transportation Research Part C: Emerging Technologies 139 (June 2022): 103645. http://dx.doi.org/10.1016/j.trc.2022.103645.

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Mohri, Hiroaki, Mikio Kubo, Masao Mori, and Yasutoshi Yajima. "A SPLIT DELIVERY VEHICLE ROUTING PROBLEM." Journal of the Operations Research Society of Japan 39, no. 3 (1996): 372–88. http://dx.doi.org/10.15807/jorsj.39.372.

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Marinelli, Mario, Leonardo Caggiani, Michele Ottomanelli, and Mauro Dell'Orco. "En route truck–drone parcel delivery for optimal vehicle routing strategies." IET Intelligent Transport Systems 12, no. 4 (May 1, 2018): 253–61. http://dx.doi.org/10.1049/iet-its.2017.0227.

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Wu, Guohua, Kexin Zhao, Jiaqi Cheng, and Manhao Ma. "A Coordinated Vehicle–Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search." Sensors 22, no. 10 (May 12, 2022): 3702. http://dx.doi.org/10.3390/s22103702.

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Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle–drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle–drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of “Drone-First, Vehicle-Then”. Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.

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Lim, Hyunpae, Gyu M. Lee, and Ivan Kristianto Singgih. "Multi-Depot Split-Delivery Vehicle Routing Problem." IEEE Access 9 (2021): 112206–20. http://dx.doi.org/10.1109/access.2021.3103640.

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Pan, Jun, Zhuo Fu, and Hongwei Chen. "Split Delivery Vehicle Routing Problem with Minimum Delivery Amounts." Journal Européen des Systèmes Automatisés 52, no. 3 (August 31, 2019): 257–65. http://dx.doi.org/10.18280/jesa.520306.

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Ajie Sukarno, Setyawan, and Yuliadi Erdani. "Desain Antarmuka Pada Vehicle Routing Problem Untuk Manajemen Armada Multi-Drone." JURNAL ILMIAH ILMU KOMPUTER 6, no. 2 (September 2, 2020): 7–14. http://dx.doi.org/10.35329/jiik.v6i2.150.

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Artikel ini membahas desain antarmuka pada vehicle routing problem (VRP) 3-dimensi untuk armada multi-drone. Armada ini melakukan perjalanan untuk mengunjungi serangkaian titik dengan memperhatikan batasan tertentu. Karena VRP diklasifikasikan sebagai masalah optimasi NP-hard, algoritma aproksimasi seperti Algoritma Genetika diterapkan untuk menemukan solusi terbaik untuk masalah optimisasi kombinatorial ini. Dalam merancang GUI ini, kami menggunakan Netlogo sebagai alat untuk membangun antarmuka, dan juga untuk eksperimen dan simulasi. Hasil penelitian ini menunjukkan bahwa dengan menggunakan Netlogo, kita dapat mendesain antarmuka untuk mensimulasikan algoritma aproksimasi dalam penyelesaian permasalahan optimasi kombinatorial, yang mudah dioperasikan oleh pengguna

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Yang, Byounghak. "A Vehicle Destination Decision Problem for the Drone-Vehicle Intermodal Delivery System." Journal of the Korean Society of Supply Chain Management 18, no. 2 (October 31, 2018): 119–31. http://dx.doi.org/10.25052/kscm.2018.10.18.2.119.

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Iranmanesh, Saeid, and Raad Raad. "A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension." Electronics 8, no. 6 (June 11, 2019): 659. http://dx.doi.org/10.3390/electronics8060659.

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Amazon, Uber Eats, and United Parcel Service (UPS) are planning to launch drone delivery services in the near future. Indeed, recently, Google has received Federal Aviation Administration (FAA) approval for its Wings delivery platform. Amazon claims that a drone logistics network is more cost-efficient and quicker than a motor vehicle delivery network. In this paper, we propose a data delivery service by the drone network in addition to parcel delivery. We propose Heuristic Flight Path Planning (HFPP) that plans a drone’s flight path based on parcel delivery destination as well as data delivery destinations (waypoints). We further extend the solution to include drone charging stations for range extension. Our simulation studies show that our proposed method has delivered the data and consignments such that HFPP delivers up to 33% more data packets compared with Encounter-Based Routing (EBR), Epidemic, and a similar path planning method. Also, HFPP reduces the data delivery delays by up to 72% while the overhead ratio is low.

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Liu, Yao, Jianmai Shi, Zhong Liu, Jincai Huang, and Tianren Zhou. "Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone." Energies 12, no. 7 (April 10, 2019): 1385. http://dx.doi.org/10.3390/en12071385.

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A novel high-voltage powerline inspection system was investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle’ route in the road network and drone’s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new Two-Layer Point-Arc Routing Problem (2L-PA-RP). Two constructive heuristics were designed based on “Cluster First, Route Second” and “Route First, Split Second”. Then, local search strategies were developed to further improve the quality of the solution. To test the performance of the proposed algorithms, different-scale practical cases were designed based on the road network and powerline network of Ji’an, China. Sensitivity analysis on the parameters related to the drone’s inspection speed and battery capacity was conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.

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Gulczynski, Damon, Bruce Golden, and Edward Wasil. "The split delivery vehicle routing problem with minimum delivery amounts." Transportation Research Part E: Logistics and Transportation Review 46, no. 5 (September 2010): 612–26. http://dx.doi.org/10.1016/j.tre.2009.12.007.

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Sze, San Nah, Siaw Ying Doreen Sek, Jeeu Fong Sze, Wai Shiang Cheah, and Kang Leng Chiew. "Vehicle Routing Problem with Simultaneous Pickup and Delivery." International Journal on Advanced Science, Engineering and Information Technology 10, no. 4 (August 6, 2020): 1360. http://dx.doi.org/10.18517/ijaseit.10.4.10234.

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Prescott-Gagnon, Eric, Guy Desaulniers, and Louis-Martin Rousseau. "Heuristics for an oil delivery vehicle routing problem." Flexible Services and Manufacturing Journal 26, no. 4 (December 11, 2012): 516–39. http://dx.doi.org/10.1007/s10696-012-9169-9.

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Meng, Shanshan, Xiuping Guo, Dong Li, and Guoquan Liu. "The multi-visit drone routing problem for pickup and delivery services." Transportation Research Part E: Logistics and Transportation Review 169 (January 2023): 102990. http://dx.doi.org/10.1016/j.tre.2022.102990.

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Dethloff, Jan. "Vehicle routing and reverse logistics: The vehicle routing problem with simultaneous delivery and pick-up." OR Spektrum 23, no. 1 (February 2001): 79–96. http://dx.doi.org/10.1007/pl00013346.

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Shi, Jianli, Jin Zhang, Kun Wang, and Xin Fang. "Particle Swarm Optimization for Split Delivery Vehicle Routing Problem." Asia-Pacific Journal of Operational Research 35, no. 02 (April 2018): 1840006. http://dx.doi.org/10.1142/s0217595918400067.

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The split delivery vehicle routing problem (SDVRP) is a variation of the capacitated vehicle routing problem in which some customers may be served by more than one vehicle. We have proposed a particle swarm optimization approach that incorporates a local search to solve the SDVRP. An integer coding method was presented, and a decoding method based on Bellman’s equation was modified for the SDVRP. A way to address the differences in the length of the velocity vector, the position vector, the personal best position vector, the local best position vector and the global best position vector was designed. Two groups of local searches for top solutions were incorporated into the algorithm, with the ability to control whether they are executed on a given solution. The algorithm was initially tested using the modified Solomon’s instances to verify the parameters used, including the local search probability, the size of the swarm, the velocity equation and the length of the vectors. Extensive computational experiments were carried out on 131 benchmark instances available in the literature. The results obtained were competitive. More precisely, equally good solutions were found in 32 instances, and improved solutions were found in 35 instances, with an average improvement of 0.02% and a maximum improvement of 1.12%.

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Azi, Nabila, Michel Gendreau, and Jean-Yves Potvin. "A dynamic vehicle routing problem with multiple delivery routes." Annals of Operations Research 199, no. 1 (October 5, 2011): 103–12. http://dx.doi.org/10.1007/s10479-011-0991-3.

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39

Sousa Matos, Marcos Raylan, Yuri Frota, and Luiz Satoru Ochi. "Green Vehicle Routing and Scheduling Problem with Split Delivery." Electronic Notes in Discrete Mathematics 69 (August 2018): 13–20. http://dx.doi.org/10.1016/j.endm.2018.07.003.

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Sabo, Cosmin, Petrică C. Pop, and Andrei Horvat-Marc. "On the Selective Vehicle Routing Problem." Mathematics 8, no. 5 (May 12, 2020): 771. http://dx.doi.org/10.3390/math8050771.

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The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

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Rautela, Anubha, S. K. Sharma, and P. Bhardwaj. "Distribution planning using capacitated clustering and vehicle routing problem." Journal of Advances in Management Research 16, no. 5 (November 21, 2019): 781–95. http://dx.doi.org/10.1108/jamr-12-2018-0113.

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Purpose The purpose of this paper is to reduce the distribution cost of an Indian cooperative dairy. The reduction of cost was achieved with the application of the clustering method (k-means clustering) and capacitated vehicle routing problem (cheapest link algorithm (CLA)). Design/methodology/approach Capacitated k-means clustering was used to split delivery locations into similar size groups (i.e. clusters) based on proximity without exceeding a specified total cluster capacity. Each cluster would be served by a local stockist. CLA was then used to find delivery routes from dairy (i.e. depot) to stockist in each cluster and from stockist to all other delivery locations within the cluster. Findings K-means clustering and CLA suggested optimal delivery routes on which vehicles will run. The complete algorithm was able to provide a solution within 30 s. Practical implications Clustering of delivery locations and use of heterogeneous fleet of delivery vehicles can result in considerable savings in daily operational cost. Originality/value Most of the research related to the use of demand clustering to improve distribution routes has been theoretical, which do not take into account real-world limitations like vehicle’s specific limitations. The authors tried to address that gap by taking a real-world case of a cooperative dairy and compared the result with existing distribution routes used by dairy. This work can be used by other dairies or distribution companies according to their scenario.

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Azis, Zainal, and Herman Mawengkang. "Time Dependent Heterogeneous Vehicle Routing Problem for Catering Service Delivery Problem." Journal of Physics: Conference Series 890 (September 2017): 012103. http://dx.doi.org/10.1088/1742-6596/890/1/012103.

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Quintana, Lisandra, Yalexa Herrera-Mena, José-Luis Martínez-Flores, Marcos Coronado, Gisela Montero, and Patricia Cano-Olivos. "DESIGN OF WASTE VEGETABLE OIL COLLECTION NETWORKS APPLYING VEHICLE ROUTING PROBLEM AND SIMULTANEOUS PICKUP AND DELIVERY." Acta logistica 7, no. 4 (December 31, 2020): 261–68. http://dx.doi.org/10.22306/al.v7i4.188.

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The growth of industrialization in Mexico has caused an increase in the demand for materials to satisfy the consumption of goods and services of a growing population. Given this scenario, there is a rise of the residual generation with affectations on the ecosystem and population health. Hence, the objective of this research was to design a network for waste vegetable oil collection based on vehicle routing problem with simultaneous pickup and delivery, starting from a distribution centre to 49 restaurants, as the generation sources of waste vegetable oil. The Vehicle Routing Problem Simultaneous Pickup and Delivery with Time Windows was the variant used as a vehicle routing method to solve the problem. The free software VPRPD was the tool used to solve the vehicle routing problem with simultaneous pickup and delivery that allowed to specify time restrictions. This software uses the simulated annealing metaheuristics in its syntax. As a result, it was obtained a total of 8 networks, for a vehicle capacity utilization of 70 percent in the 6 t vehicle and 46 percent in the 8 t vehicle.

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Díaz-Parra, Ocotlán, Jorge A. Ruiz-Vanoye, Beatriz Bernábe Loranca, Alejandro Fuentes-Penna, and Ricardo A. Barrera-Cámara. "A Survey of Transportation Problems." Journal of Applied Mathematics 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/848129.

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This paper aims at being a guide to understand the different types of transportation problems by presenting a survey of mathematical models and algorithms used to solve different types of transportation modes (ship, plane, train, bus, truck, Motorcycle, Cars, and others) by air, water, space, cables, tubes, and road. Some problems are as follows: bus scheduling problem, delivery problem, combining truck trip problem, open vehicle routing problem, helicopter routing problem, truck loading problem, truck dispatching problem, truck routing problem, truck transportation problem, vehicle routing problem and variants, convoy routing problem, railroad blocking problem (RBP), inventory routing problem (IRP), air traffic flow management problem (TFMP), cash transportation vehicle routing problem, and so forth.

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Zhang, Yan, Chunhui Yuan, and Jiang Wu. "Vehicle Routing Optimization of Instant Distribution Routing Based on Customer Satisfaction." Information 11, no. 1 (January 9, 2020): 36. http://dx.doi.org/10.3390/info11010036.

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Since the actual factors in the instant distribution service scenario are not considered enough in the existing distribution route optimization, a route optimization model of the instant distribution system based on customer time satisfaction is proposed. The actual factors in instant distribution, such as the soft time window, the pay-to-order mechanism, the time for the merchant to prepare goods before delivery, and the deliveryman’s order combining, were incorporated in the model. A multi-objective optimization framework based on the total cost function and time satisfaction of the customer was established. Dual-layer chromosome coding based on the deliveryman-to-node mapping and the access order was conducted, and the nondominated sorting genetic algorithm version II (NSGA-II) was used to solve the problem. According to the numerical results, when time satisfaction of the customer was considered in the instant distribution routing problem, the customer satisfaction increased effectively and the balance between customer satisfaction and delivery cost in the means of Pareto optimization were obtained, with a minor increase in the delivery cost, while the number of deliverymen slightly increased to meet the on-time delivery needs of customers.

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Zhu, Yanfei, Chunhui Li, and Kwang Y. Lee. "The NR-EGA for the EVRP Problem with the Electric Energy Consumption Model." Energies 15, no. 10 (May 17, 2022): 3681. http://dx.doi.org/10.3390/en15103681.

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Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.

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Zhang, Jie, Yifan Zhu, Xiaobo Li, Mengjun Ming, Weiping Wang, and Tao Wang. "Multi-Trip Time-Dependent Vehicle Routing Problem with Split Delivery." Mathematics 10, no. 19 (September 27, 2022): 3527. http://dx.doi.org/10.3390/math10193527.

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Motivated by some practical applications of post-disaster supply delivery, we study a multi-trip time-dependent vehicle routing problem with split delivery (MTTDVRP-SD) with an unmanned aerial vehicle (UAV). This is a variant of the VRP that allows the UAV to travel multiple times; the task nodes’ demands are splittable, and the information is time-dependent. We propose a mathematical formulation of the MTTDVRP-SD and analyze the pattern of the solution, including the delivery routing and delivery quantity. We developed an algorithm based on the simulation anneal (SA) framework. First, the initial solution is generated by an improved intelligent auction algorithm; then, the stochastic neighborhood of the delivery route is generated based on the SA algorithm. Based on this, the model is simplified to a mixed-integer linear programming model (MILP), and the CPLEX optimizer is used to solve for the delivery quantity. The proposed algorithm is compared with random–simulation anneal–CPLEX (R-SA-CPLEX), auction–genetic algorithm–CPLEX (A-GA-CPLEX), and auction–simulation anneal–CPLEX (A-SA) on 30 instances at three scales, and its effectiveness and efficiency are statistically verified. The proposed algorithm significantly differs from R-SA-CPLEX at a 99% confidence level and outperforms R-SA-CPLEX by about 30%. In the large-scale case, the computation time of the proposed algorithm is about 30 min shorter than that of A-SA. Compared to the A-GA-CPLEX algorithm, the performance and efficiency of the proposed algorithm are improved. Furthermore, compared to a model that does not allow split delivery, the objective function values of the solution of the MTTDVRP-SD model are reduced by 52.67%, 48.22%, and 34.11% for the three scaled instances, respectively.

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Rajappa, Gautham Puttur, Joseph H. Wilck, and John E. Bell. "An Ant Colony Optimization and Hybrid Metaheuristics Algorithm to Solve the Split Delivery Vehicle Routing Problem." International Journal of Applied Industrial Engineering 3, no. 1 (January 2016): 55–73. http://dx.doi.org/10.4018/ijaie.2016010104.

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Split Delivery Vehicle Routing Problem (SDVRP) is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) that allows the same customer to be served by more than one vehicle. Existing literature has applied Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to other variants of VRP but no known research effort has applied ACO or a combination of ACO and GA to solve the Split Delivery Vehicle Routing Problem (SDVRP). Hence, two algorithms using ACO and hybrid metaheuristics algorithm comprising a combination of ACO, Genetic Algorithm (GA) and heuristics is proposed and tested on existing benchmark SDVRP problems. The results indicate that the two proposed algorithms are competitive in both solution quality and solution time and for some problem instances, the best ever solutions have been found.

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Karkula, Marek. "Practical aspects of the split delivery vehicle routing problem (SDVRP)." WUT Journal of Transportation Engineering 120 (March 1, 2018): 155–66. http://dx.doi.org/10.5604/01.3001.0014.4769.

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Transport process arrangement and delivery route planning is one of the most important tasks of managers in distribution, trade and production enterprises. The problem of route planning concerns the rationalization of product distribution processes offered by company for the customer's network. In operational research, such a problem is included in the class of issues of Vehicle Routing Problem – VRP. The VRP delivery planning problems constitute a wide family of issues arising primarily from the conditions and constraints of the practice. The paper presents the practical application of one of the VRP variants – the problem of arranging routes for the Split Delivery Vehicle Routing Problem – SDVRP, and the results of analyses based on research carried out in a distribution company.

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Chu, Andrej. "Ant Colony Optimization Method and Split-Delivery Vehicle Routing Problem." Communications - Scientific letters of the University of Zilina 11, no. 4 (December 31, 2009): 38–42. http://dx.doi.org/10.26552/com.c.2009.4.38-42.

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Journal articles on the topic 'Vehicle routing problem with drone delivery'

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