Relevant bibliographies by topics / Vehicle routing problem with drone delivery

Academic literature on the topic 'Vehicle routing problem with drone delivery'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Vehicle routing problem with drone delivery"

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Slaviša, Dumnić. "Модел оптимизације доставе пошиљака у системима са хетерогеним доставним возилима." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=110781&source=NDLTD&language=en.

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Машинско учење и неуронске мреже су алати који налазе све већупримену у решавању практичних проблема. За креирање неуронскемреже потребан је скуп података, који може бити прикупљен наразличите начине. У овој тези је показано да се подаци за тренингнеуронске мреже могу успешно прикупити креирањем веб игре.Сакупљени скуп података садржи стратегије решавања проблематрговачког путника и проблема рутирања возила.
Mašinsko učenje i neuronske mreže su alati koji nalaze sve većuprimenu u rešavanju praktičnih problema. Za kreiranje neuronskemreže potreban je skup podataka, koji može biti prikupljen narazličite načine. U ovoj tezi je pokazano da se podaci za treningneuronske mreže mogu uspešno prikupiti kreiranjem veb igre.Sakupljeni skup podataka sadrži strategije rešavanja problematrgovačkog putnika i problema rutiranja vozila.
Machine learning and neural networks are the tools that are finding more andmore fields of application in solving practical problems. For the creation ofthe neural networks, data can be successfully collected by creating a webgame. The data collected in this manner has strategic solutions for theproblems of Travel salesperson problem and vehicle routing problem.
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Liu, Kai. "A study on the Split Delivery Vehicle Routing Problem." Diss., Mississippi State : Mississippi State University, 2005. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Min, Hokey. "The vehicle routing problem with product/spatial consolidation and backhauling." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1314819600.

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Aleman, Rafael E. "A Guided Neighborhood Search Applied to the Split Delivery Vehicle Routing Problem." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1237575805.

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Cetiner, Selim. "An Iterative Hub Location And Routing Problem For Postal Delivery Systems." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/4/759399/index.pdf.

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In this study, we consider the Turkish postal delivery system and develop an effective solution approach for the combined hub location and routing problem where the location of hub nodes are determined, the nonhub regional postal offices are allocated to the hubs, and the optimal set of routes are determined for each hub. Since the realized post-routing distances between origin-destination pairs are different from those used in the hub-location model, we develop an algorithm that finds the route-compatible hub configuration and allocation paths. The algorithm is the one that iterates between the hub-location phase and a routing phase. Our strategy consists of updating the distances used in the first phase in order to produce a solution that contains the cognition of routes. Some special structures in the routed network are also identified and used for improving the solution. Computational experience is reported.
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Mohamed, Nurum Huda binti. "Hybridisation of heuristics and exact methods for the split delivery vehicle routing problem." Thesis, University of Kent, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.591103.

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Due to the worldwide of petrol prices crises that have been rising inevitably over the past years, any reduction in the transportation cost will benefit most companies. The purpose of this research is to contribute in solving this problem by alleviating some afthis burden. This thesis is about the Split Delivery Vehicle Routing Problem (SDVRP) and some of its variants. The SDVRP is a relaxed version of the classical VRP where customers can be visited more than once. It is also applicab le to problems with customers' demands larger than the vehicle capacity. These types of split routing problems can be found app licable in many real-world logistical problems. The total cost and the number of vehicles required could be reduced by allowing split deliveries. The savings are found to be the most in those problems with customers' demands nearly as large as the vehicle capacity limit. Some constructive heuristics adapted from the savings, the sweep and the insertion methods are first put forward to solve the SDVRP. These sets of solutions are then used in several set covering-based models which are proposed to so lve the problem. Both the heuristics and the set covering-based approaches are tested on two large sets of published data set from the literature with encouraging results. Similarly on the second data set, 12 best solutions are found from the 42 instances with an average deviation of 1.37%. As split deliveries may increase customers' administration cost and inconvenience, some models to cater for this drawback are proposed and their implementations are tested and evaluated against the classical SDVRP for guidance. This thesis is organised as follows: In the first chapter, a brief overview of logistics and distribution management, in particular with respect to vehicle routing, is provided. A brief explanation about the TSP, the VRP and its variants is also presented. Methodologies for solving these kinds of problems are also given. This is then followed by an example to illustrate the benefit of sp lit deliveries in Chapter 2 together with the SDVRP mathematical formulations and a review of the methods used to tackle the SDVRP and its variants. A brief introduction to the two benchmark SDVRP data sets used is given at the end of this chapter. Several constructive heuristics are first implemented in the third chapter. These are adapted from the Savings, the Sweep and the Insertion methods to include split deliveries. These methods are coded in C++ Language and tested on the two SDVRP data sets taken from the literature and their performance is evaluated against the best published resu lts showing encouraging results. In Chapter 4, an existing mathematical programming approach based on the set covering is implemented using ILOO CPLEX Callable Library. This approach uses several sets of routes from the obtained solutions by the constructive heuristics in Chapter 3. New mathematical ILP based models are adapted and a pi lot test is conducted within a limited time to select the most appropriate model and corresponding CPLEX parameters. New best results are discovered by the selected model on the two large data sets. In Chapter 5, the dual information relating to both customers and routes, is obtained by relaxing the selected set covering-based problem as a Linear Programming. The customers' dual information is incorporated in both the classical insertion cost formula and the well-known savings formulae and is proved to be beneficial. Route reduction schemes are then designed to inc lude those promising routes only. At the end of this chapter, a new set of routes is then constructed by combining the new generated routes and those subsets of routes found previously. This set is then used in the selected model where some new best results are obtained. A new variant of the SDVRP is introduced in Chapter 6 where an incentive scheme is incorporated to overcome the inconvenience of those affected customers that have split deliveries. Two new models are then explored and adapted from the previous set covering-based models. Given that no published results exist for these variants, as a guide, those results originated from the classical SDVRP are used for comparison purpose. Our find ings and suggestions for potential future research are highlighted in the final chapter.
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Nowak, Maciek A. "The Pickup and Delivery Problem with Split Loads." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7223.

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This dissertation focuses on improvements in vehicle routing that can be gained by allowing multiple vehicles to service a common load. We explore how costs can be reduced through the elimination of the constraint that a load must be serviced by only one vehicle. Specifically, we look at the problem of routing vehicles to service loads that have distinct origins and destinations, with no constraint on the amount of a load that a vehicle may service. We call this the Pickup and Delivery Problem with Split Loads (PDPSL). We model this problem as a dynamic program and introduce structural results that can help practitioners implement the use of split loads, including the definition of an upper bound on the benefit of split loads. This bound indicates that the routing cost can be reduced by at most one half when split loads are allowed. Furthermore, the most benefit occurs when load sizes are just above one half of vehicle capacity. We develop a heuristic for the solution of large scale problems, and apply this heuristic to randomly generated data sets. Various load sizes are tested, with the experimental results supporting the finding that most benefit with split loads occurs for load sizes just above one half vehicle capacity. Also, the average benefit of split loads is found to range from 6 to 7% for most data sets. The heuristic was also tested on a real world example from the trucking industry. These tests reveal the benefit of both using split loads and allowing fleet sharing. The benefit for split loads is not as significant as with the random data, and the various business rules added for this case are tested to find those that have the most impact. It is found that an additional cost for every stop the vehicle makes strictly limits the potential for benefit from split loads. Finally, we present a simplified version of the PDPSL in which all origins are visited prior to any destination on a route, generalizing structural results from the Split Delivery Vehicle Routing Problem for this problem.
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Rahmani, Younes. "The Multi-product Location-Routing Problem with Pickup and Delivery." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0331/document.

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Dans les problèmes de localisation-routage classiques (LRP), il s'agit de combiner des décisions stratégiques liées aux choix des sites à ouvrir (centres de traitement) avec des décisions tactiques et opérationnelles liées à l'affectation des clients aux sites sélectionnés et a la confection des tournées associées. Cette thèse propose de nouveaux modèles de localisation-routage permettant de résoudre des problématiques issues de réseaux logistiques, devenus aujourd'hui de plus en plus complexes vu la nécessité de mutualisation de ressources pour intégrer des contraintes de développement durable et des prix de carburants qui semblent augmenter de manière irrémédiable. Plus précisément, trois aspects ont été intégrés pour généraliser les modèles LRP classiques de la littérature : 1) l'aspect pickup and delivery, 2) l'aspect multi-produits, et 3) la possibilité de visiter un ou plusieurs centres de traitement dans une tournée donnée. Nous avons étudié deux schémas logistiques, qui ont donné lieu à deux nouveaux modèles de localisation et de routage, le MPLRP-PD (LRP with multi-product and pickup and delivery), qui peut être vu comme une extension des problèmes de tournées de véhicules avec collecte et livraison, intégrant une décision tactique liée à la localisation des centres de traitement (noeud avec collecte et livraison) dans un réseau de distribution à un seul échelon, et le 2E-MPLRP-PD (Two-echelon LRP with multi-product and pickup and delivery) qui est une généralisation du LRP à deux échelons avec les contraintes citées plus-haut. Ces deux modèles ont été formalisés par des programmes linéaires en variables mixtes (MIP). Des techniques de résolution, basées sur des méthodes de type heuristique, clustering, métaheuristique, ont été proposées pour résoudre le MPLRP-PD et le 2E-MPLRP-PD. Les jeux d'essais de la littérature ont été généralisés pour tester et valider les algorithmes proposés
In the framework of Location-Routing Problem (LRP), the main idea is to combine strategic decisions related to the choice of processing centers with tactical and operational decisions related to the allocation of customers to selected processing centers and computing the associated routes. This thesis proposes a new location-routing model to solve problems which are coming from logistics networks, that became nowadays increasingly complex due to the need of resources sharing, in order to integrate the constraints of sustainable development and fuels price, which is increasing irreversibly. More precisely, three aspects have been integrated to generalize the classical LRP models already existed in the literature: 1) pickup and delivery aspect, 2) multi-product aspect, and 3) the possibility to use the processing centers as intermediate facilities in routes. We studied two logistics schemes gives us two new location-routing models: (i) MPLRP-PD (Multi-product LRP with pickup and delivery), which can be viewed as an extension of the vehicle routing problem with pick-up and delivery, including a tactical decision related to the location of processing centers (node with pick-up and delivery), and (ii) 2E-MPLRP-PD (Two-echelon multi-product LRP with pickup and delivery), which is a generalization of the two-echelon LRP. Both models were formalized by mixed integer linear programming (MIP). Solving techniques, based on heuristic methods, clustering approach and meta-heuristic techniques have been proposed to solve the MPLRP-PD and the 2E-MPLRP-PD. The benchmarks from the literature were generalized to test and to validate the proposed algorithms
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Rahimi, Mohammad. "Inventory routing problem under dynamic, uncertain and green considerations." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI049/document.

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La gestion des stocks et la maîtrise de la distribution sont les deux activités importantes dans le management de la chaîne logistique. L’optimisation simultanée de ces deux activités est connue sous l’intitulé du problème de gestion de stock et de tournée de livraison (Inventory Routing Problem, IRP). L’IRP traditionnelle est confronté aux différents problèmes, causé principalement par le manque d'informations complètes et/ou temps réel, tels que les changements de la demande, l’embouteillage soudain causé par un accident, etc. Le partage et la mise à jour d'information logistique peut améliorer l'efficacité d’IRP. De plus, en raison de la spécificité de l'IRP dans la logistique urbaine, il est important de considérer d'autres critères comme les critères sociaux, environnementaux et le niveau de service qui pourraient être en conflictuel. L’objectif principal de cette thèse est de développer des modèles et des méthodes des IRP avec la prise en compte des incertitudes, du niveau de service et de l’impact environnemental, social en finalement les informations du temps réel (IRP dynamique). Dans cette thèse, trois modèles mathématiques sont proposés. Le premier modèle multi-objectif est pour identifier un compromis entre le niveau de service, les critères environnementaux et économiques. Pour gérer des paramètres incertains, on applique une approche floue. Dans le deuxième modèle, nous avons étudié l'impact des critères sociaux sur les IRPs en proposant un modèle mathématique bi-objectif. Une approche stochastique basée sur des scénarios est développée pour faire face à l'incertitude dans le modèle. Enfin, le troisième model concerne l'impact de l'utilisation d'informations du temps réel dans les IRP. Il est à noter que, selon la durée de vie du produit tant sur le plan financier que sur le plan écologique, les produits périssables sont considérés dans les trois modèles proposés. Les résultats montrent une gestion dynamique est beaucoup plus efficace que la statique
The inventory management and transportation are two main activities of supply chain management. The joint optimization of these two activities is known as Inventory Routing Problem (IRP). The main objective of IRP is to determine the set of retailers to be delivered to in each period, the delivery sequence for each vehicle, and the quantities of goods delivered to each retailer for each period of a planning horizon. The traditional IRPs are faced different problems, caused mainly by lack of complete and/or timely information such as shifts in demand, traffic caused by a sudden vehicles accident, etc. sharing of updated and reliable logistics information can meaningful improve the efficiency of IRP. Moreover, because of the specificity of IRP in urban logistic, it is important to tack into account other criteria as social, environmental criteria and service level that could be in conflict. The main objective of this thesis is to (i) choose appropriate social, environmental and service level criteria, (ii) integrate them in mathematical models, and (iii) study the impact of these criteria on dynamic optimization of IRPs for perishable products under uncertain parameters. For this purpose, three mathematical models are proposed. The first model is multi-objective mathematical model in order to make a trade-off between service level, environmental criteria and economic. To decrease quantity of expired products, a nonlinear step function as holding cost function is integrated in the model. Moreover, to solve the problem a fuzzy possibilistic approach is applied to handle uncertain parameters. In the second model, a bi-objective mathematical model is proposed to study impact of social issues on the IRPs. In the proposed model, first objective function concerns economic criteria while the second one social issues. A scenario-based stochastic approach is developed to cope with uncertainty in the model. Finally, the third model concerns impact of using real-time information in efficiency of IRPs. It is noteworthy that, according significant role of perishable products in the both financially and ecology sides of IRPs, perishable products are considered in all three proposed model while even proposed models are appropriate to nonperishable ones as well. The results show that a dynamic management is more efficient than the static one
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Kaja, Sai Chandana. "A New Approach for Solving the Disruption in Vehicle Routing Problem During the Delivery : A Comparative Analysis of VRP Meta-Heuristics." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19576.

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Context. The purpose of this research paper is to describe a new approach for solving the disruption in the vehicle routing problem (DVRP) which deals with the disturbance that will occur unexpectedly within the distribution area when executing the original VRP plan. The paper then focuses further on the foremost common and usual problem in real-time scenarios i.e., vehicle-breakdown part. Therefore, the research needs to be accomplished to deal with these major disruption in routing problems in transportation. Objectives. The study first investigates to find suitable and efficient metaheuristic techniques for solving real-time vehicle routing problems than an experiment is performed with the chosen algorithms which might produce near-optimal solutions. Evaluate the performance of those selected algorithms and compare the results among each other. Methods. To answer research questions, firstly, a literature review has been performed to search out suitable meta-heuristic techniques for solving vehicle routing problems. Then based on the findings an experiment is performed to evaluate the performance of selected meta-heuristic algorithms. Results. Results from the literature review showed that the meta-heuristic approaches such as. Tabu Search, Ant Colony Optimization, and Genetic Algorithmare suitable and efficient algorithms for solving real-time vehicle routing problems. The performance of those algorithms has been calculated and compared with one another with standard benchmarks. Conclusions. The performance of a Tabu Search algorithm is best among the other algorithms, followed by Ant Colony Optimization and Genetic Algorithm. Therefore, it has been concluded that the Tabu Search is the best algorithm for solving real-time disruption problems in VRP. The results are similar to the performance comparison of the selected algorithms and standard benchmarks are presented within the research.
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Book chapters on the topic "Vehicle routing problem with drone delivery"

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Kim, Yonggab, Hoyoung Jung, and Seokcheon Lee. "Drone Delivery Vehicle Routing Problem with Multi-flight Level." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 43–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85906-0_5.

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Ouiss, Mustapha, Abdelaziz Ettaoufik, Abdelaziz Marzak, and Abderrahim Tragha. "A Parallel Genetic Algorithm for Solving the Vehicle Routing Problem with Drone Medication Delivery." In Advances on Smart and Soft Computing, 225–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5559-3_19.

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Schermer, Daniel. "Integration of Drones in Last-Mile Delivery: The Vehicle Routing Problem with Drones." In Operations Research Proceedings, 17–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18500-8_3.

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Jeong, Ho Young, and Seokcheon Lee. "Collaborative Hybrid Delivery System: Drone Routing Problem Assisted by Truck." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 33–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85906-0_4.

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Das, Aparna, Claire Mathieu, and Shay Mozes. "The Train Delivery Problem - Vehicle Routing Meets Bin Packing." In Approximation and Online Algorithms, 94–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18318-8_9.

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Zhang, Chenghao, and Ming Cheng. "Electric vehicle routing problem with simultaneous pickup and delivery." In Frontier Research: Road and Traffic Engineering, 613–21. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003305002-81.

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Vornhusen, Benedikt, and Herbert Kopfer. "Emission Vehicle Routing Problem with Split Delivery and a Heterogeneous Vehicle Fleet." In Lecture Notes in Computer Science, 76–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24264-4_6.

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Pewira Redi, A. A. N., Muhammad Reza Chandra Kusuma, Bertha Maya Sopha, Anna Maria Sri Asih, and Rahmad Inca Liperda. "Two-Echelon Vehicle Routing Problem for Agricultural Area Mapping Operation with Drone." In Smart and Sustainable Supply Chain and Logistics — Challenges, Methods and Best Practices, 173–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15412-6_11.

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Ochelska-Mierzejewska, Joanna. "Ant Colony Optimization Algorithm for Split Delivery Vehicle Routing Problem." In Advanced Information Networking and Applications, 758–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44041-1_67.

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Deng, Xuefei, Jing Liang, Lu Che, Lei Zhang, and Rong Sun. "Optimization of Multi-compartment Vehicle Routing Problem in Delivery Mode." In Proceedings of the Fifth International Forum on Decision Sciences, 11–22. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7817-0_2.

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Conference papers on the topic "Vehicle routing problem with drone delivery"

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Zudio, Anderson, Igor Machado Coelho, and Luiz Satoru Ochi. "Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-107.

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The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.
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Liang, Yiwei, Dawei Hu, and Xiangqun Du. "Research on Routing Problem Based on Vehicle-Drone Joint Distribution—Taking Emergency Drugs Delivery as an Example." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483053.406.

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Yang, Qian-qian, Da-wei Hu, Hong-fan Chu, and Chao-ran Xu. "An Electric Vehicle Routing Problem with Pickup and Delivery." In 18th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481523.017.

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Gao, Zhao, Guohua Sun, and Zhenglei Yuan. "Genetic Algorithm to the Split Delivery Vehicle Routing Problem." In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010082.

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Perwira Redi, A. A. N., Rahmad Inca Liperda, Bertha Maya Sopha, Anna Maria Sri Asih, Nandini Niramaya Sekaringtyas, and Handina Boedhy Astiana. "Relief Mapping Assessment using Two-Echelon Vehicle Routing Problem with Drone." In 2020 6th International Conference on Science and Technology (ICST). IEEE, 2020. http://dx.doi.org/10.1109/icst50505.2020.9732812.

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Jiajia, Zheng, Liu Guorong, Gu Zhenyu, and Bai Xiaohui. "Delivery vehicle routing problem with simultaneous delivery and pickup in E-commerce environment." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978189.

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Sombuntham, Pandhapon, Voratas Kachitvichyanukul, Sio-Iong Ao, Hideki Katagir, Li Xu, and Alan Hoi-Shou Chan. "Multi-depot Vehicle Routing Problem with Pickup and Delivery Requests." In IAENG TRANSACTIONS ON ENGINEERING TECHNOLOGIES VOLUME 5: Special Edition of the International MultiConference of Engineers and Computer Scientists 2009. AIP, 2010. http://dx.doi.org/10.1063/1.3510581.

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Shi, Jianli. "Models and Heuristics for the Split Delivery Vehicle Routing Problem." In Fifth International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479384.307.

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He, Xiaonian. "On the Split Delivery Vehicle Routing Problem Based on Python." In 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM). IEEE, 2022. http://dx.doi.org/10.1109/mlccim55934.2022.00039.

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Soeanu, Andrei, Sujoy Ray, Mourad Debbabi, Jean Berger, Abdeslem Boukhtouta, and Ahmed Ghanmi. "A decentralized heuristic for multi-depot split-delivery vehicle routing problem." In 2011 IEEE International Conference on Automation and Logistics (ICAL). IEEE, 2011. http://dx.doi.org/10.1109/ical.2011.6024686.

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