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Journal articles on the topic 'Clustering and vehicle routing'
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Praveen, V., V. Hemalatha, and M. Poovizhi. "An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (2017): 919–24. http://dx.doi.org/10.31142/ijtsrd4701.
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Akhand, M. A. H., Tanzima Sultatana, M. I. R. Shuvo, and Al-Mahmud Al-Mahmud. "Constructive and Clustering Methods to Solve Capacitated Vehicle Routing Problem." Oriental journal of computer science and technology 10, no. 3 (2017): 549–62. http://dx.doi.org/10.13005/ojcst/10.03.02.
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Vehicle Routing Problem (VRP) is a real life constraint satisfaction problem to find minimal travel distances of vehicles to serve customers. Capacitated VRP (CVRP) is the simplest form of VRP considering vehicle capacity constraint. Constructive and clustering are the two popular approaches to solve CVRP. A constructive approach creates routes and attempts to minimize the cost at the same time. Clarke and Wright’s Savings algorithm is a popular constructive method based on savings heuristic. On the other hand, a clustering based method first assigns nodes into vehicle wise cluster and then generates route for each vehicle. Sweep algorithm and its variants and Fisher and Jaikumar algorithm are popular among clustering methods. Route generation is a traveling salesman problem (TSP) and any TSP optimization method is useful for this purpose. In this study, popular constructive and clustering methods are studied, implemented and compared outcomes in solving a suite of benchmark CVRPs. For route optimization, Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Velocity Tentative Particle Swarm Optimization (VTPSO) are employed in this study which are popular nature inspired optimization techniques for solving TSP. Experimental results revealed that parallel Savings is better than series Savings in constructive method. On the other hand, Sweep Reference Point using every stop (SRE) is the best among clustering based techniques.
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González Vargas, Guillermo, and Felipe González Aristizábal. "Metaheuristics applied to vehicle routing. A case study. Part 3: Genetic Clustering and Tabu Routing." Ingeniería e Investigación 27, no. 2 (2007): 106–13. http://dx.doi.org/10.15446/ing.investig.v27n2.14838.
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This paper presents hybrid meta-heuristics called Genetic Clustering and Tabu Routing for solving a vehicle routing problem using two phases methodology: first clustering and then routing. The results are compared with those obtained using meta-heuristics and heuristic techniques presented in previous papers. Genetic clustering and Tabu routing average results were 23% and 9.1% better, respectively.
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V., Praveen, Hemalatha V., and Poovizhi M. "An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem." International Journal of Trend in Scientific Research and Development 1, no. 6 (2017): 919–24. https://doi.org/10.31142/ijtsrd4701.
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An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customer's location or the path where the customer's resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi "An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: https://www.ijtsrd.com/papers/ijtsrd4701.pdf
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Yu, Li Zhe, Tiao Juan Ren, Zhang Quan Wang, and Ban Teng Liu. "Research on Vehicle Networking Clustering Routing Algorithm Based on Subtractive Clustering." Applied Mechanics and Materials 644-650 (September 2014): 2366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2366.
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Vehicle networking is an important part of intelligent traffic system. It combines wireless sensor network and mobile autonomous network, which is better for drivers to obtain road condition information to ensure driving safety. This paper focuses on the analysis of subtractive clustering and proposed clustering routing algorithm based on subtractive clustering in order to reduce communication of wireless sensor network and redundant flooding and routing expanse. In the algorithm, cluster head selection adopts subtractive clustering to produce cluster node in node intensive place. Cluster forms by adopting current non cluster head node mechanism which reduced energy consumption. Then the maintenance method of cluster is proposed. At last, it introduces routing protocol of cluster which is easier for clustering structure to manage and synchronize network.
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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 (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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Ma, Yinpei, Liyan Geng, and Meihong Zhu. "Two-Layer Location-Routing Problem Based on Heuristic Hybrid Algorithm." Mathematical Problems in Engineering 2023 (May 18, 2023): 1–10. http://dx.doi.org/10.1155/2023/7335443.
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Location-routing problem (LRP) thoroughly considers location allocation problem (LAP) and vehicle routing problem (VRP) which has been an integral part applied in modern logistics. A number of researchers at home and aboard have put forward their views by establishing fine models. On the basis of studying the previous research results by classification, summary, and comparative analysis, this study hence proposes a new solution-fuzzy clustering model and algorithm to resolve two-layer location-routing problem based on a heuristic hybrid algorithm: Designing a hybrid genetic and simulated annealing algorithm (GASA) to optimize the initial value of the fuzzy C-means clustering algorithm (FCM); considering the roving visit characteristics of vehicles to design the path by employing a special VRP problem—the multiple traveling salesman problem (MTSP). Theoretical analysis and experimental results show that the algorithm used in this study has the advantages of fast convergence speed and less iterations, which significantly improve the quality of the initial solution of FCM in LAP, shorten the vehicle patrol cycle in VRP to a great extent, improve the vehicle utilization, and save the vehicle patrol costs. A specific example is programmed by MATLAB to verify the feasibility of this method.
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Pal Singh, Tejinder, and Er Nitika Kapoor. "Clustering and multicasting scheme for fog computing based vehicular ad hoc network." International Journal of Engineering & Technology 7, no. 2.27 (2018): 82. http://dx.doi.org/10.14419/ijet.v7i2.27.12613.
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The vehicular adhoc network is the data oriented in network in which information should be passed with least delay. The vehicle adhoc network is the decentralized type of network in which vehicles to vehicle and vehicle to road side communication is possible. The Fog computing is the advance computing scheme to store small amount of data. The routing is the major issue of vehicular adhoc network due to high mobility of the vehicle nodes. In this research work, multicasting based routing scheme is proposed for path establishment from source to destination. In the proposed scheme root vehicles are selected for path establishment from source to destination. When the data will be received on the road side unit then k-mean clustering will be applied which divide data into clusters which define that either it can be saved on cloud or on fog server based on quantity of data. The performance of proposed scheme is testing in NS2 and proposed scheme performs well in terms of PDR and route lifetime
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W. Rizkallah, L., M. F. Ahmed, and N. M. Darwish. "A clustering algorithm for solving the vehicle routing assignment problem in polynomial time." International Journal of Engineering & Technology 9, no. 1 (2020): 1. http://dx.doi.org/10.14419/ijet.v9i1.22231.
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The Vehicle Routing Problem (VRP) consists of a group of customers that needs to be served. Each customer has a certain demand of goods. A central depot having a fleet of vehicles is responsible for supplying the customers with their demands. The problem is composed of two sub-problems: The first sub-problem is an assignment problem where both the vehicles that will be used as well as the customers assigned to each vehicle are determined. The second sub-problem is the routing problem in which for each vehicle having a number of cus-tomers assigned to it, the order of visits of the customers is determined. Optimal number of vehicles as well as optimal total distance should be achieved. In this paper, an approach for solving the first sub-problem, the assignment problem, is presented. In the approach, a clustering algorithm is proposed for finding the optimal number of vehicles by grouping the customers into clusters where each cluster is visited by one vehicle. This work presents a polynomial time clustering algorithm for finding the optimal number of clusters. Also, a solution to the assignment problem is provided. The proposed approach was evaluated using Solomon’s C1 benchmarks where it reached optimal number of clusters for all the benchmarks in this category. The proposed approach succeeds in solving the assignment problem in VRP achieving a solving time that surpasses the state-of-the-art approaches provided in the literature. It also provides a means of working with varying num-ber of customers without major increase in solving time.
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Zhang, Yi, and Jixian Zhang. "Design and Optimization of Cluster-Based DSRC and C-V2X Hybrid Routing." Applied Sciences 12, no. 13 (2022): 6782. http://dx.doi.org/10.3390/app12136782.
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With the continuous development of connected and automated vehicles (CAVs) and Internet of Vehicle (IoV) technologies, various application scenarios have put forward higher requirements for vehicular communications. On the one hand, applications related to vehicle driving safety require lower latency and higher throughput. On the other hand, users who use cellular vehicle-to-everything (C-V2X) to transfer data will have to face high communication fees due to the increasing amount of data. Therefore, from the perspective of balancing quality of service (QoS) and user communication costs, this paper integrates dedicated short-range communication (DSRC) and C-V2X, two vehicular communication technologies with their own advantages, into a framework called cluster-based traffic differentiated hybrid routing (CTDHR), to provide services for in-vehicle communication. A vehicle clustering method based on hierarchical clustering is proposed to solve problems (e.g., the communication linking being difficult to maintain and the frequent cell handover due to high-speed movement of vehicles). The CTDHR framework is modeled on the resulting clusters and an objective equation was established. Finally, since the obtained objective equation is a nonlinear integer programming problem, we propose a heuristic algorithm to solve this optimization problem. In the simulation experiments, CTDHR shows better communication performance than the existing DSRC and C-V2X hybrid models. The experimental results show that CTDHR can reduce the communication costs of users while satisfying QoS.
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Xie, Xiaoyun, Yahya Dorostkar Navaei, and Sajad Einy. "A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET." Wireless Communications and Mobile Computing 2023 (June 14, 2023): 1–18. http://dx.doi.org/10.1155/2023/3776815.
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In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly improved compared to the previous methods in terms of package delivery rate. Also, the proposed method with a total delay of 24.566 ms has a shorter delay than the previous methods in terms of message sending delay in the network.
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Siddiqa, Ayesha, Muhammad Diyan, Muhammad Toaha Raza Khan, Malik Muhammad Saad, and Dongkyun Kim. "Mitigating Broadcasting Storm Using Multihead Nomination Clustering in Vehicular Content Centric Networks." Electronics 10, no. 18 (2021): 2270. http://dx.doi.org/10.3390/electronics10182270.
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Vehicles are highly mobile nodes; therefore, they frequently change their topology. To maintain a stable connection with the server in high-speed vehicular networks, the handover process is restarted again to satisfy the content requests. To satisfy the requested content, a vehicular-content-centric network (VCCN) is proposed. The proposed scheme adopts in-network caching instead of destination-based routing to satisfy the requests. In this regard, various routing protocols have been proposed to increase the communication efficiency of VCCN. Despite disruptive communication links due to head vehicle mobility, the vehicles create a broadcasting storm that increases communication delay and packet drop fraction. To address the issues mentioned above in the VCCN, we proposed a multihead nomination clustering scheme. It extends the hello packet header to get the vehicle information from the cluster vehicles. The novel cluster information table (CIT) has been proposed to maintain several nominated head vehicles of a cluster on roadside units (RSUs). In disruptive communication links due to the head vehicle’s mobility, the RSU nominates the new head vehicle using CIT entries, resulting in the elimination of the broadcasting storm effect on disruptive communication links. Finally, the proposed scheme increases the successful communication rate, decreases the communication delay, and ensures a high cache success ratio on an increasing number of vehicles.
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Le, Thi Diem Chau, Duc Duy Nguyen, Judit Oláh, and Miklós Pakurár. "CLUSTERING ALGORITHM FOR A VEHICLE ROUTING PROBLEM WITH TIME WINDOWS." Transport 37, no. 1 (2022): 17–27. http://dx.doi.org/10.3846/transport.2022.16850.
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The demand for daily food purchases has increased dramatically, especially during the Covid-19 pandemic. This requires suppliers to face a huge and complex problem of delivering products that meet the needs of their customers on a daily basis. It also puts great pressure on managers on how to make day-to-day decisions quickly and efficiently to both satisfy customer requirements and satisfy capacity constraints. This study proposes a combination of the cluster-first –route-second method and k-means clustering algorithm to deal with a large Vehicle Routing Problem with Time Windows (VRPTW) in the logistics and transportation field. The purpose of this research is to assist decision-makers to make quick and efficient decisions, based on optimal costs, the number of vehicles, delivery time, and truck capacity efficiency. A distribution system of perishable goods in Vietnam is used as a case study to illustrate the effectiveness of our mathematical model. In particular, perishable goods include fresh products of fish, chicken, beef, and pork. These products are packed in different sizes and transferred by vehicles with 1000 kg capacity. Besides, they are delivered from a depot to the main 39 customers of the company with arrival times following customers’ time window. All of the data are collected from a logistics company in Ho Chi Minh city (Vietnam). The result shows that the application of the clustering algorithm reduces the time for finding the optimal solutions. Especially, it only takes an average of 0.36 s to provide an optimal solution to a large Vehicle Routing Problem (VRP) with 39 nodes. In addition, the number of trucks, their operating costs, and their utilization are also shown fully. The logistics company needs 11 trucks to deliver their products to 39 customers. The utilization of each truck is more than 70%. This operation takes the total costs of 6586215.32 VND (Vietnamese Dong), of which, the transportation cost is 1086215.32 VND. This research mainly contributes an effective method for enterprises to quickly find the optimal solution to the problem of product supply.
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Wang, Yong, Jiayi Zhe, Xiuwen Wang, Yaoyao Sun, and Haizhong Wang. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows." Sustainability 14, no. 11 (2022): 6709. http://dx.doi.org/10.3390/su14116709.
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Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.
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Pasha, Soran A. "RVMF: RELIABLE ROUTING METHOD FOR VEHICULAR AD HOC NETWORKS USING MOTH-FLAME AND FIREFLY OPTIMIZATION ALGORITHMS." Science Journal of University of Zakho 11, no. 2 (2023): 220–26. http://dx.doi.org/10.25271/sjuoz.2023.11.2.1005.
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With the advancement of wireless communication technology, the intelligent transportation system (ITS) has attracted the attention of vehicle companies and academic researchers. Recently, vehicular ad hoc networks (VANETs) as a leading genuine technology have received serious attention as a kind of mobile ad hoc network (MANET) to ensure the safety of vehicles, drivers, and passengers. However, these networks face many challenges due to the mobility of vehicle nodes, wireless communication, and frequent topology changes. One of the crucial issues of these networks is a cluster-based routing scheme with the ability to provide quality of service (QoS) parameters. A clustering scheme is an appropriate method for increasing the scalability of VANETs. In a cluster-based routing scheme, the cluster head (CH) is responsible for receiving data from its member nodes, and aggregating and transferring data to the next CH node. On the other hand, providing a suitable clustering method is NP-hard problems and meta-heuristic algorithms are suitable for solving these problems. A scalable and reliable routing scheme is necessary and essential in VANETs. In this paper, a routing method based on the clustering technique is presented considering the moth-flame optimization (MFO) algorithm for clustering and the Firefly optimization algorithm (FoA) with a suitable fitness function for routing between CHs. The simulation of the proposed method with MATLAB software shows that the proposed RVMF method improves the parameters of packet delivery rate (PDR), latency, and throughput.
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Bührmann, Jacoba, and Frances Bruwer. "K-MEDOID PETAL-SHAPED CLUSTERING FOR THE CAPACITATED VEHICLE ROUTING PROBLEM." South African Journal of Industrial Engineering 32, no. 2 (2021): 33–41. http://dx.doi.org/10.7166/32-3-2610.
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In this research, k-medoid clustering is modelled and evaluated for the capacitated vehicle routing problem (CVRP). The k-medoid clustering method creates petal-shaped clusters, which could be an effective method to create routes in the CVRP. To determine routes from the clusters, an existing metaheuristic — the ruin and recreate (R&R) method — is applied to each generated cluster. The results are benchmarked to those of a well-known clustering method, k-means clustering. The performance of the methods is measured in terms of travel cost and distance travelled, which are well-known metrics for the CVRP. The results show that k-medoid clustering method outperforms the benchmark method for most instances of the test datasets, although the CVRP without any predefined clusters still provides solutions that are closer to optimal. Clustering remains a reliable distribution management tool and reduces the processing requirements of large-scale CVRPs.
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Deepak, Kumar Malviya, and Shrivastav Prashant. "Enhanced Performance for Military Vehicles Using Clustering Algorithm in VANET." International Journal of Trend in Scientific Research and Development 3, no. 1 (2018): 882–88. https://doi.org/10.31142/ijtsrd19102.
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Vehicular Ad hoc networks are established between mobile vehicles equipped with wireless interfaces that could be either of heterogeneous or homogeneous nature. Vehicles and road side fixed equipment's both of them can be either private belonging to individuals or companies or public means of transport service providers. In this network every participating vehicle works as single node or wireless router, allowing cars which are 100 to 300 m from each other to connect and create a network with a large range. Since vehicle node in VANET are quick moving element, so the route among the nodes breaks much of the time. In this paper, clustering performed for the transmission of the data from the source to destination. This technique increases the go with the flow rate of data packets inside the network and decreases the end to end delay. Deepak Kumar Malviya | Prashant Shrivastav "Enhanced Performance for Military Vehicles Using Clustering Algorithm in VANET" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19102.pdf
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Khan, Zahid, Anis Koubaa, Sangsha Fang, Mi Young Lee, and Khan Muhammad. "A Connectivity-Based Clustering Scheme for Intelligent Vehicles." Applied Sciences 11, no. 5 (2021): 2413. http://dx.doi.org/10.3390/app11052413.
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The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks.
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Hammouti, Issam El, Khaoula Derqaoui, and Mohamed El Merouani. "A modified clustering search based genetic algorithm for the proactive electric vehicle routing problem." International Journal of Industrial Engineering Computations 14, no. 4 (2023): 609–22. http://dx.doi.org/10.5267/j.ijiec.2023.9.004.
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In this paper, an electric vehicle routing problem with time windows and under travel time uncertainty (U-EVRW) is addressed. The U-EVRW aims to find the optimal proactive routing plan of the electric vehicles under the travel time uncertainty during the route of the vehicles which is rarely studied in the literature. Furthermore, customer time windows, limited loading capacities and limited battery capacities constraints are also incorporated. A new mixed integer programming (MIP) model is formulated for the proposed U-EVRW. In addition to the commercial CPLEX Optimizer version 20.1.0, a modified Clustering Search based Genetic algorithm (MCSGA) is developed as a solution method. Numerical tests are conducted on the one hand to validate the effectiveness of the proposed MCSGA and on the other hand to analyze the impact of travel time uncertainty of the electric vehicle on the solutions quality.
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Ji, Xiang, Huiqun Yu, Guisheng Fan, Huaiying Sun, and Liqiong Chen. "Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks." Mobile Information Systems 2018 (July 30, 2018): 1–15. http://dx.doi.org/10.1155/2018/9826782.
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Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITSs). The current researches are intensely focusing on the problems of routing protocol reliability and scalability across the urban VANETs. Vehicle clustering is testified to be a promising approach to improve routing reliability and scalability by grouping vehicles together to serve as the foundation for ITS applications. However, some prominent characteristics, like high mobility and uneven spatial distribution of vehicles, may affect the clustering performance. Therefore, how to establish and maintain stable clusters has become a challenging problem in VANETs. This paper proposes a link reliability-based clustering algorithm (LRCA) to provide efficient and reliable data transmission in VANETs. Before clustering, a novel link lifetime-based (LLT-based) neighbor sampling strategy is put forward to filter out the redundant unstable neighbors. The proposed clustering scheme mainly composes of three parts: cluster head selection, cluster formation, and cluster maintenance. Furthermore, we propose a routing protocol of LRCA to serve the infotainment applications in VANET. To make routing decisions appropriate, we nominate special nodes at intersections to evaluate the network condition by assigning weights to the road segments. Routes with the lowest weights are then selected as the optimal data forwarding paths. We evaluate clustering stability and routing performance of the proposed approach by comparing with some existing schemes. The extensive simulation results show that our approach outperforms in both cluster stability and data transmission.
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Hamacher, Anja, Winfried Hochstättler, and Christoph Moll. "Tree partitioning under constraints — clustering for vehicle routing problems." Discrete Applied Mathematics 99, no. 1-3 (2000): 55–69. http://dx.doi.org/10.1016/s0166-218x(99)00125-0.
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Potvin, Jean-Yves, and Christian Robillard. "Clustering for vehicle routing with a competitive neural network." Neurocomputing 8, no. 2 (1995): 125–39. http://dx.doi.org/10.1016/0925-2312(94)00012-h.
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Laha, Dipak, and Ankan Bose. "Efficient Clustering-Based Constructive Heuristics for Capacitated Vehicle Routing." International Journal of Mathematics in Operational Research 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijmor.2023.10056523.
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Bose, Ankan, and Dipak Laha. "Efficient clustering-based constructive heuristics for capacitated vehicle routing." International Journal of Mathematics in Operational Research 27, no. 4 (2024): 458–78. http://dx.doi.org/10.1504/ijmor.2024.138465.
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Bhandari, Umesh. "Solving Vehicle Routing Problem using Machine Learning based clustering and TSP Cluster Redistribution." International Journal of Research and Review 9, no. 10 (2022): 344–51. http://dx.doi.org/10.52403/ijrr.20221041.
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Vehicle Routing Problem (VRP) is a well-known challenging nondeterministic polynomial-time hard (NP-hard) problem in logistics domain. Time window based VRP is an extension of the problem. The basic problem is to identify optimal set of routes for a fleet of vehicles to traverse to deliver to a given set of customers in a specified time duration. The objective is to minimize the costs of traversed routes. This paper proposes a machine learning and TSP based cluster redistribution approach to solve the time window based VRP. The proposed approach consists of three phases: machine learning based cluster creation, TSP based cluster routes and cluster re-distribution. The results demonstrate the efficacy and optimality of the proposed solution. Keywords: Vehicle Routing Problem (VRP), Time window based VRP, Machine learning
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Mengistu, Dereje Dejene, M. Srinivasa Rao, and Prof V. V. S. Kesava Rao. "Green Vehicle Routing Under Customer Demand Uncertainty." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 36–45. http://dx.doi.org/10.35940/ijrte.a5861.0910321.
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customer satisfaction is the main focus area in supply chain management and the distribution of goods plays a vital role in customer satisfaction. cost optimization and in time delivery leads to customer satisfaction. Optimization of Vehicle route plan is the method generally applied to deal it. such plans shall consider the minimization of pollution emissions. This paper proposes a optimization method to handle the vehicle routing problem(vrp). Genetic algorithm and fuzzy clustering algorithm are applied in the method.
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Dereje, DejeneMengistu, Srinivasa Rao M., and Rao V.V.S.Kesava. "Green Vehicle Routing Under Customer Demand Uncertainty." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (2021): 36–45. https://doi.org/10.35940/ijrte.A5861.0910321.
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customer satisfaction is the main focus area in supply chain management and the distribution of goods plays a vital role in customer satisfaction. cost optimization and in time delivery leads to customer satisfaction. Optimization of Vehicle route plan is the method generally applied to deal it. such plans shall consider the minimization of pollution emissions. This paper proposes a optimization method to handle the vehicle routing problem(vrp). Genetic algorithm and fuzzy clustering algorithm are applied in the method.
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Comert, Serap Ercan, Harun Resit Yazgan, and Gamze Turk. "Hopfield neural network based on clustering algorithms for solving green vehicle routing problem." International Journal of Industrial Engineering Computations 13, no. 4 (2022): 573–86. http://dx.doi.org/10.5267/j.ijiec.2022.6.002.
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As a result of the rapidly increasing distribution network, the toxic gases emitted by the vehicles to the environment have also increased, thus posing a threat to health. This study deals with the problem of determining green vehicle routes aiming to minimize CO2 emissions to meet customers' demand in a supermarket chain that distributes fresh and dried products. A new method based on clustering algorithms and Hopfield Neural Network is proposed to solve the problem. We first divide the large-size green vehicle routing problem into clusters using the K-Means and K-Medoids algorithms, and then the routing problem for each cluster is found using the Hopfield Neural Network, which minimizes CO2 emissions. A real-life example is carried out to illustrate the performance and applicability of the proposed method. The research concludes that the proposed approach produces very encroaching results.
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Pan, Xiaoming, Yong Wu, and Gao Chong. "Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load." Security and Communication Networks 2022 (July 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/8199991.
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In the distribution scenario, the using cost of vehicles is closely related to energy consumption, and the energy consumption rate of a vehicle is closely related to the size of its load. The traditional vehicle routing optimization model takes the shortest distance as the optimization goal when the customer demand is determined, while the influence of the random demand and the changing load on the energy consumption and cost of vehicles in the process of distribution is ignored. Therefore, in this paper, load varying vehicle routing problem with stochastic demands (LVGVRPSD) model is proposed with the goal of minimizing transportation energy consumption and considering the load variability and the randomness of customer demand. K-means clustering algorithm is combined with ant colony optimization (ACO) to solve the problem, and the constraint of risk probability is introduced to describe the vehicle overload problem. Examples in the standard vehicle routing problem test data set are provided and analyzed. LVGVRPSD is also compared with the traditional capacitated vehicle routing problem (CVRP) model. The case study results show that the vehicle energy consumption can be reduced by 2% in the model that considers changing load compared to the model that does not consider changing load. The results illustrate that the method of path optimization is more advantageous and reasonable in the pursuit of reducing energy consumption, when the changing load and the random demand of customer are considered.
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Devred, Thomas, Martine Wahl, and Patrick Sondi. "A Comparison of Backbone and Mesh Clustering Strategies for Collaborative Management of 6G Vehicle-to-Vehicle Exchanges." Electronics 13, no. 3 (2024): 572. http://dx.doi.org/10.3390/electronics13030572.
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Sixth-generation (6G) announcements promise the best performance not only for latency but also for the number of connected objects. These characteristics particularly suit intelligent transport system (ITS) applications involving a large number of moving vehicles with stringent latency constraints. Moreover, in the 6G era, these applications will often operate while relying on direct cooperation and exchanges between vehicles, in addition to centralized services through a telecommunication infrastructure. Therefore, addressing collaborative intelligence for ad hoc routing protocols that ensure efficient management of multihop vehicle-to-vehicle communications is mandatory. Among the numerous organization models proposed in the literature, the chain–branch–leaf (CBL), a virtual backbone-like model, has demonstrated best performance regarding latency against the state-of-the-art approaches. However, its structure, which lacks redundancy, may lead to higher data loss in the case of the failure of one of the relaying branch nodes. This study investigated how the multipoint relay (MPR) technique—which is intrinsically redundant—used in the optimized link state routing (OLSR) protocol can be efficiently adapted to the road traffic context, especially by restricting MPR selection to a single traffic flow direction (TFD-OLSR). The simulation results confirmed that CBL-OLSR obtains the least end-to-end delay for various types of application traffic due to its efficient reduction in the number of relays and the amount of routing traffic. However, despite higher routing traffic, TFD-OLSR improves the delivery rate, especially for more than two-hop communications, thus demonstrating the benefits of its redundancy property.
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Ghaffar, Muhammad Arslan, Lei Peng, Muhammad Umer Aslam, Muhammad Adeel, and Salim Dassari. "Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies." Electronics 13, no. 18 (2024): 3650. http://dx.doi.org/10.3390/electronics13183650.
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In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones unreachable by conventional vehicles, thereby identifying areas requiring UAV delivery. Furthermore, we categorize the demand for medical supplies into two distinct sets based on vehicle accessibility, optimizing distribution routes via both UAVs and vehicles. Through comparative analysis, our findings reveal that the artificial bee colony (ABC) algorithm significantly outperforms the genetic algorithm in terms of solving efficiency, iteration counts, and delivery speed. However, the ABC algorithm’s tendency toward early local optimization and rapid convergence leads to potential stagnation in local optima. To mitigate this issue, we incorporate a simulated annealing technique into the ABC framework, culminating in a refined optimization approach which successfully overcomes the limitations of premature local optima convergence. The experimental results validate the efficacy of our enhanced algorithm, demonstrating reduced iteration counts, shorter computation times, and substantially improved solution quality over traditional logistic models. The proposed method holds promise for significantly improving the operational efficiency and service quality of the healthcare system’s logistics during critical situations.
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Gummadi, Annapurna, and Dr K Raghava Rao. "EECLA: Clustering and Localization Techniques to Improve Energy Efficient Routing in Vehicle Tracking using Wireless Sensor Networks." International Journal of Engineering & Technology 7, no. 2.7 (2018): 926. http://dx.doi.org/10.14419/ijet.v7i2.7.11425.
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Abstract: The applications of wireless sensor networks became more usable in daily life. In spite of many proposed techniques and methods, energy efficient routing in WSN is still an open issue. In this paper we made an attempt to give one of the solution for this problem in vehicle tracking system based on the vehicle sensor nodes. We studied many existing works, were failed in handling location and energy efficient routing of vehicle tracking properly. We proposed an algorithm which handles clustering and location at time and improves the performance of the system. This algorithm uses the fundamentals of LEACH, CLAEER and mean shifted algorithm. We conducted a sequence of experiments and our algorithm EECLA (Clustering and Localization Techniques to Improve Energy Efficient Routing in Wireless Sensor Networks) has given better results than the existed one with more accuracy.
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Singh, Varimna, L. Ganapathy, and Ashok K. Pundir. "An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems." International Journal of Information Systems and Supply Chain Management 12, no. 4 (2019): 1–26. http://dx.doi.org/10.4018/ijisscm.2019100101.
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The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.
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Zhao, Chong, Jianghong Han, Xu Ding, Lei Shi, and Fan Yang. "An Analytical Model for Interference Alignment in Broadcast Assisted VANETs." Sensors 19, no. 22 (2019): 4988. http://dx.doi.org/10.3390/s19224988.
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Application of safety-related information interaction among vehicles has always been a research frontier in Vehicular Ad-hoc NETworks (VANETs). These messages require high real-time performance. There is a lot of research dependant on creating optimization model for communication task scheduling or routing protocols to reduce communication delay. In this paper, we analyze characteristics of safety-related information and introduce Interference Alignment (IA) technology in VANETs. To further improve routing efficiency, a data-driven assisted transmission routing and broadcast model framework for Vehicle to Vehicle(V2V) and Vehicle to Infrastructure (V2I) communication are constructed which are the basis for IA. Depending on the proposed model, we propose an optimization problem of minimizing total number of time slots required for safety information sharing in VANETs. Then a clustering algorithm is designed to narrow feasible solution space. Simulation results show that the approach can effectively reduce the number of time slots required and improve link use by 20% percent compared with no IA applied.
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Liu, Yixiao, Lei Zhang, Yixuan Zhou, Qin Xu, Wen Fu, and Tao Shen. "Clustering-Based Decision Tree for Vehicle Routing Spatio-Temporal Selection." Electronics 11, no. 15 (2022): 2379. http://dx.doi.org/10.3390/electronics11152379.
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The algorithm of the clustering-based decision tree, which is a methodology of multimodal fusion, has made many achievements in many fields. However, it is not common in the field of transportation, especially in the application of automobile navigation. Meanwhile, the concept of Spatio-temporal data is now widely used. Therefore, we proposed a vehicle routing Spatio-temporal selection system based on a clustering-based decision tree. By screening and clustering Spatio-temporal data, which is a collection of individual point data based on historical driving data, we can identify the routes and many other features. Through the decision tree modeling of the state information of Spatio-temporal data, which includes the features of the historical data and route selection, we can obtain an optimal result, that is, the route selection made by the system. Moreover, all the above calculations and operations are done on the edge, which is different from the vast majority of current cloud computing vehicle navigation. We have also experimented with our system using real vehicle data. The experiments show that it can output path decision results for a given situation, which takes little time and is the same as the approximated case of networked navigation. The experiments yielded satisfactory results. Our system could save a lot of cloud computing power, which might change the current navigation systems.
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Ha, Je-Min, and Geeju Moon. "An Application of k-Means Clustering to Vehicle Routing Problems." Journal of Society of Korea Industrial and Systems Engineering 38, no. 3 (2015): 1–7. http://dx.doi.org/10.11627/jkise.2015.38.3.01.
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Liu, Shouchen, and Cheng Zhang. "Optimization of Cold Chain Distribution Route with Mixed Time Window considering Customer Priority." Computational Intelligence and Neuroscience 2022 (September 9, 2022): 1–18. http://dx.doi.org/10.1155/2022/2953205.
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In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.
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Tariq, Rehan, Zeshan Iqbal, and Farhan Aadil. "IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization." Wireless Communications and Mobile Computing 2020 (November 7, 2020): 1–29. http://dx.doi.org/10.1155/2020/8860646.
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Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid.
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Zhu, Ji. "Solving Capacitated Vehicle Routing Problem by an Improved Genetic Algorithm with Fuzzy C-Means Clustering." Scientific Programming 2022 (February 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/8514660.
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Aiming at solving the vehicle routing problem, an improved genetic algorithm based on fuzzy C-means clustering (FCM) is proposed to solve the vehicle routing problem with capacity constraints. On the basis of genetic algorithm, the FCM algorithm is used to decompose the large-scale vehicle routing optimization problem into small-scale subproblems, which can effectively improve the efficiency of the algorithm. At the same time, a generation method of the initial solution to CVRP problem is designed. The improved algorithm has good robustness and can also reduce the possibility of falling into local optimization in the search process. Finally, a simulation example is provided to verify the efficiency and superiority of the proposed algorithm.
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Poohoi, Ratchadakorn, Kanate Puntusavase, and Shunichi Ohmori. "Stas crossover with K-mean clustering for vehicle routing problem with time window." Decision Science Letters 13, no. 3 (2024): 525–34. http://dx.doi.org/10.5267/j.dsl.2024.5.008.
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Vehicle Routing Problem (VRP) is important in the transportation and logistics industries. Vehicle Routing Problem with Time Window (VRPTW) is a kind of VRP with the additional time windows constraint in the model and is classified as an NP-hard problem. In this study, we proposed Stas crossover in Genetic Algorithm (GA) to solve VRPTW by developing the problem with K-mean clustering. The experiments use the standard Solomon’s benchmark problem instances for VRPTW. The results with K-mean clustering are shown to perform better for minimum distance and average distance than without K-mean clustering. In the case of location and dispersion characteristics of the customer, the paths with K-mean clustering are arranged into groups and are orderly, but the paths without K-mean clustering are disordered. After that, this paper shows the comparison of the crossover operator performance on instances of Solomon benchmark, and appropriate crossover operators are recommended for each type of problem. The results of the proposed algorithm are better than the best-known solutions from the previous studies for some instances. Moreover, our proposed research will serve as a guideline for a real-world case study.
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Ebadinezhad, Sahar, Ziya Dereboylu, and Enver Ever. "Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)." Sustainability 11, no. 9 (2019): 2624. http://dx.doi.org/10.3390/su11092624.
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The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.
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Yin, Ruyang, and Peixia Lu. "A Cluster-First Route-Second Constructive Heuristic Method for Emergency Logistics Scheduling in Urban Transport Networks." Sustainability 14, no. 4 (2022): 2301. http://dx.doi.org/10.3390/su14042301.
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Advanced strategies for emergency logistics scheduling problems in urban transport networks have been a challenging topic for centuries. This study proposed a cluster-first route-second constructive heuristic method based on the continuous approximation (CA) for ‘one-to-many’ vehicle routing to dispatch commidities after an emergency. The objective of the study is to provide a replenish schedule and routing solution from the government/provider’s end in order to minimize the total motion cost, pipeline inventory cost, and holding cost with backorder for the disaster relief operation. The developed method can turn the complicated vehicle routing problem (VRP) into a relatively simple travel salesman problem (TSP) for pre-assigned customer sets. The CA is employed to determine the optimal replenish amount and inventory level for the route serving a given location. The Christofides method is then applied to solve the TSP for the selected cluster. Two clustering methods are investigated in this research: (1) a local-based approach where clustering and routing are determined; and (2) a K-mean clustering method where points are clustered upfront by the CA solution. A case study in Miami-Dade County in Florida to dispatch fuels from the depot to 72 gas stations is presented, demonstrating the proposed approach and comparing two clustering methods. The numerical results illustrate the effectiveness of the algorithms and conclude that the local-based clustering approach may yield a lower total cost with a higher motion cost.
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Guezouli, Lahcene, Mohamed Bensakhria, and Samir Abdelhamid. "Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows." International Journal of Applied Evolutionary Computation 9, no. 1 (2018): 1–16. http://dx.doi.org/10.4018/ijaec.2018010101.
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In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.
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Petr, Stodola, and Nohel Jan. "Adaptive Ant Colony Optimization with Node Clustering for the Multi-Depot Vehicle Routing Problem." IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 27, no. 6 (2023): 1866–80. https://doi.org/10.5281/zenodo.7341076.
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Ali, Wajid, and Shalini Ninoria. "Enhanced route reliability algorithm for vehicular ad hoc networks." International Journal on Information Technologies and Security 17, no. 2 (2025): 101–10. https://doi.org/10.59035/jphw2885.
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Vehicular Ad Hoc Networks (VANETs) play a crucial role in facilitating road communication. By transmitting real-time traffic information to nearby vehicles and infrastructure, VANETs help address various real-world challenges, enhancing traffic management and road safety. To achieve this a secure and reliable scheme is needed. Reliable communication in VANER struggles with many problems like varying speed of vehicles, dynamic vehicle movement etc. To address this, we propose the Clustering Based Enhanced Route Reliability Algorithm (ERRAV), which uses clustering technique to improve route stability. In this geographical area is divided into clusters, each cluster is managed by a Cluster Head (CH). CH is selected based on signal strength, mobility, and vehicle density, ensuring stable communication. After clustering Reliable routes are established and maintained through periodic link monitoring, route updates, and backup routes. Simulations of ERRAV show the enhancement of scalability by reducing congestion, optimizing route discovery, and adaptation to network changes. EERAV been compared to AODV, DSR, and OLSR and experimental results show the reduced routing overhead, improves packet delivery, and minimizes link failures, making it highly effective in mobile environments.
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Cruz-Chávez, Marco Antonio, and Alina Martínez-Oropeza. "Feasible Initial Population with Genetic Diversity for a Population-Based Algorithm Applied to the Vehicle Routing Problem with Time Windows." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3851520.
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A stochastic algorithm for obtaining feasible initial populations to the Vehicle Routing Problem with Time Windows is presented. The theoretical formulation for the Vehicle Routing Problem with Time Windows is explained. The proposed method is primarily divided into a clustering algorithm and a two-phase algorithm. The first step is the application of a modifiedk-means clustering algorithm which is proposed in this paper. The two-phase algorithm evaluates a partial solution to transform it into a feasible individual. The two-phase algorithm consists of a hybridization of four kinds of insertions which interact randomly to obtain feasible individuals. It has been proven that different kinds of insertions impact the diversity among individuals in initial populations, which is crucial for population-based algorithm behavior. A modification to the Hamming distance method is applied to the populations generated for the Vehicle Routing Problem with Time Windows to evaluate their diversity. Experimental tests were performed based on the Solomon benchmarking. Experimental results show that the proposed method facilitates generation of highly diverse populations, which vary according to the type and distribution of the instances.
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Alrubaye, Jaafar Sadiq, and Behrouz Shahgholi Ghahfarokhi. "Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks." PLOS ONE 18, no. 10 (2023): e0293662. http://dx.doi.org/10.1371/journal.pone.0293662.
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5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline.
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S, Dr Neelambike, and Varun B K. "A Weight-Based Clustering Algorithm is Used by Military Vehicles for VANET Communication." ASM Science Journal 20, no. 1 (2025): 1–9. https://doi.org/10.32802/asmscj.2025.1573.
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Every vehicle node in a vehicular ad hoc network (VANET) denotes a mobile node that serves as an information transmitter, receiver, and router. VANET belongs to the mobile ad hoc network (MANET) subgroup and is associated with dynamic topology. Finding a viable solution for all VANET applications is the researchers' main task because dynamic network situations provide more complex problems than MANET topologies do. Cluster-based, geocast-based, topology-based, position-based, and broadcast-based routing protocols make up the six categories of routing protocols used in VANET. Unmanned military vehicles (UMVs) and autonomous robots are used in the modern warfare strategy to carry out risky military combat tasks. The military vehicles (MVs) exchange information with one another in order to complete the necessary military missions as a group. The suggested work uses a weight-based clustering technique to partition a rhombus-shaped area into numerous clusters for the purpose of communicating event data to the cars. Rhombus-shaped areas at intersections are particularly useful for clustering. Real-time average speed and degree are two weighted measures that were employed in the suggested method to select the cluster head (CH). The right CH can be selected in the network with the help of this effort. Instead of broadcasting the data, each car in a cluster sends it to the CH. The network performance for various protocols, such as Ad-hoc on-demand distance vector (AODV) and dynamic source routing (DSR), has been simulated using the SUMO and NETSIM simulators. This performance is shown in terms of packet delivery ratio, throughput, delay, overhead transmission, mean, and standard deviation. According to the proposed weight-based clustering algorithm, the assignments of the weights are based upon two parameters: vehicle speed and degree. The speed corresponds to the instantaneous speed of a vehicle, while the degree corresponds to the number of nearby vehicles that the sensors are unable to communicate with. The vehicle, which has the highest weight for the combined factors is made the cluster head (CH). The weight is adjusted dynamically in order to adapt to the changing speed and the number of active neighbours in real-time. The weight is attained using the following formula: WT(i)= w1 × deg(i) + w2 × μn; where deg(i) corresponds to degree (number of neighbouring vehicles), and μn, normalised speed. The weighting factors w1 = 0.4 and w2 = 0.6 are set so that the degree has lesser effect than speed in the consideration of selection of the cluster head.
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Thangiah, Sam R., and Said Salhi. "Genetic clustering: An adaptive heuristic for the multidepot vehicle routing problem." Applied Artificial Intelligence 15, no. 4 (2001): 361–83. http://dx.doi.org/10.1080/08839510151087293.
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Ewbank, Henrique, Peter Wanke, Henrique L. Correa, and Otávio Figueiredo. "The capacitated vehicle routing problem revisited: using fuzzy c-means clustering." International Journal of Logistics Systems and Management 34, no. 4 (2019): 411. http://dx.doi.org/10.1504/ijlsm.2019.10025129.
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