Dijkstra Algorithm Research Articles

RFID car using arduino mega 2560 by dijkstras algorithm

This paper introduces a module which is used to transport goods or people from one place to another without any driver assistant. It is mainly used in big industries to save the time and energy. This module is built around an RFID sensor. RFID technology uses fields of electromagnetic waves to track and monitor tags attached to objects. When triggered with a field of electromagnetic waves investigate pulse from an adjacent RFID reader device, this tag delivers digital information back to the reader, which is often an inventory number. This number can be used to keep track of inventories. The sensors collect the data and sends to the main algorithm and then it takes the decision which way to go, we implemented our car to be completely automated and does not required any instructions from human. To achieve this we have chosen the shortest path algorithm know as Dijkstra's algorithm.

Optimization of Collective Irrigation Network Layout through the Application of the Analytic Hierarchy Process (AHP) Multicriteria Analysis Method

On numerous occasions, we often have very little information or must make a decision considering qualitative aspects that are difficult to evaluate. This study focuses on obtaining objective criteria to assist in decision-making in the design phase of pressurized water pipes in collective irrigation networks. In the layout of these networks, various types of paths and roads for laying pipes can be encountered, and it is not always a simple task to obtain the least costly layout or the one with the fewest issues during construction. In order to obtain objective results, different layout alternatives are evaluated using the Analytic Hierarchy Process (AHP) Multicriteria Analysis Methodology and the Dijkstra algorithm to obtain optimal solutions. This is applied to twelve case studies where the types of available layout paths are identified as alternatives, and four criteria are established for their evaluation. Recognized experts in irrigation modernization conduct the evaluation to derive weighting coefficients for selecting the optimal layout. The coefficients or resistances obtained weigh the lengths of the pipes, allowing the selection of the most suitable alternative based on the defined criteria. The results are compared with a network designed by an expert using classical methodologies, revealing cost improvements in the design phase and a reduction in conflict points, thus leading to faster execution of the works.

Research on the Intelligent Planning of Mine Fire Evacuation Routes Based on a Multifactor Coupling Analysis

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Routing and dynamic core allocation with fragmentation optimization in EON-SDM

The increasing need for rapid data transmission in optical networks has made the effective allocation of spectrum resources a crucial necessity. This study presents a new spectrum allocation approach that incorporates path selection and careful consideration of cross-talk (XT) limitations. We propose a novel fragmentation coefficient and dynamic core-changing-based routing, spectrum, and core allocation (FC-DCC-RSCA) technique. The algorithm begins by searching for the shortest paths using the Dijkstra algorithm. It then proceeds to evaluate fragmentation coefficients (FC) using the Continuous Aligned Slot Ratio (CASR) model. Subsequently, the FC undergoes a filtration process wherein only those falling inside predetermined fragmentation ranges (min and maxFC) are retained. Further spectrum allocation has been done based on XT constraints. The algorithm has better proficiency in dynamic flexibility, enabling it to effectively handle varying traffic loads. By conducting a series of rigorous simulations, our proposed approach exhibits a notable decrease in bandwidth blocking probability (BBP) while also opening the door for considerable improvements in spectrum utilization. The algorithm’s proficiency in adapting to changing network circumstances signifies its importance as a helpful thing in enhancing the allocation of resources within optical networks. It is well-positioned to address the developing requirements of contemporary communication systems. The spectrum utilization of the FC-DCC-RSCA increases by 12.5% and 6.25%, exhibits a reduction in its FR by 13.5% and 2.7%, and has a BBP of 0.051 for the traffic load of 450 Erlang, compared to SBP-PMC and FMDE-RSCA. The algorithm that has been provided provides opportunities for more investigation and enhancement. Potential areas for future research include the examination of strategies to improve energy efficiency, the development of scalable solutions for bigger network infrastructures, and the incorporation of machine learning methodologies to facilitate dynamic decision-making processes. Furthermore, it is worth investigating the potential of the method to be applied in developing optical communication technologies, such as space-division multiplexing. These endeavors hold the potential to enhance the current level of knowledge and practices in the field of optical network management and optimization.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

To address the problems of low network centralized management, weak interaction, limited hardware scalability, compatibility issues and challenges in expansion in the static deployment of traditional in-vehicle networks (IVNs), a new IVN architecture is designed. At the same time, to better meet the IVN Quality of Service (QoS) requirements and improve the real-time guarantee of data transmission, the QoS routing optimization mechanism under the new architecture is established. First, a new IVN architecture including a forwarding plane, control plane and application plane is designed by introducing software defined network (SDN) technology and combining it with the IVN itself. Second, the end-to-end delay optimization model of IVN is established by introducing network calculus theory, defining system parameters, creating a network model, calculating latency, considering queuing and congestion. the traditional routing algorithm is improved, and the DBROA algorithm has been proposed, which enhances the performance of QoS routing by introducing features such as distributed routing decisions, beacon mechanisms, optimization algorithms, and adaptability. This improvement allows it to better meet the QoS requirements of various applications and services, thereby enhancing existing QoS routing algorithms. Finally, an IVN routing optimization system is built and implemented, and the performance of different algorithms is compared and analyzed. The experimental results show that compared with the traditional Dijkstra and ECMP algorithms, the DBROA algorithm can effectively reduce the data forwarding delay and packet loss rate, improve the overall performance of IVN, and provide better QoS guarantees for IVN real-time data transmission.

Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems

Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.

Multi-Autonomous Mobile Robot traffic Management Based on Layered Costmaps and a modified Dijkstra's Algorithm

In recent times, the use of autonomous mobile robots (AMRs) has increased in many industries. As more AMRs roam the same area, traffic management becomes essential to prevent congestion and deadlocks. In related work, traffic management is often achieved using sophisticated, centralised planning approaches, albeit this often suffers from scalability issues. This paper therefore explores an approach where multi-AMR traffic management is achieved with layered costmaps and a modified Dijkstra's algorithm. This keeps the global path planner in the individual AMRs, thus not suffering scalability issues. To achieve multi-AMR coordination, traffic lanes and restricted areas are added to the AMRs’ global costmaps. Furthermore, the AMRs also use a modified Dijkstra's algorithm that supports implementation of traffic directions. A proof-of-concept solution is implemented in Robot Operating System 2 with Nav2 and Gazebo. The implemented solution was tested against a standard solution without any traffic management in three scenarios designed to provoke collisions. The results indicate that the implemented solution can prevent a set of collisions better than one without traffic management.

Dijkstra Algorithm Implementation to Determine the Shortest Route to Hospital: A Case Study in Magelang District Indonesia

Digital maps are an important tool used to find a place. The use of digital maps is very diverse, including to provide routes from one place to another. The map can also show that the route is heavy with traffic so that another route can be found. However, sometimes maps provide unclear routes so users can get lost. Users will search for the nearest hospital on the map. The map will display all hospitals, but the user must choose the hospital to go to and then follow the suggested route. This is of course very inconvenient if in an emergency you have to find the location of the nearest hospital, so you need an application that can show the exact location of the hospital. The purpose of this research is to determine the shortest route to the nearest hospital so as to save time in an emergency. The method used in this research is using Dijkstra's algorithm. Current location search and the shortest route to the nearest hospital. The calculation results obtained a value of 0.476 km which indicates the shortest route. The application of Dijkstra's algorithm can maximize the use of the map with the shortest route to the nearest hospital.

Fermatean Shortest Route Problem with Interval Fermatean Neutrosophic Fuzzy Arc Length: Formulation and a Modified Dijkstra’s Algorithm

Dijkstra’s algorithm (DA) is a very popular approach for finding the shortest route (SR) in the shortest route problem (SRP). The SRP becomes a challenging and complex problem in real life scenarios. The Fermatean neutrosophic set is a mathematical model that combines Fermatean sets with neutrosophic sets. It can handle the unclear, ambiguous, inconsistent, confusing, and uncertain information that comes from real-world problems. Decision-makers face difficulty accurately determining the precise membership (MG) and non membership levels due to the lack of appropriate data available. The FNS can handle this problem. In this study, we consider the interval FNS to describe the arc weight of a neutrosophic graph (NG). This SRP is called an interval Fermatean neutrosophic shortest route problem (IFNSRP). A modified DA is presented to solve this IFNSRP in an uncertain environment. The effectiveness of the presented method is illustrated with a numerical instance of a neutrosophic network.

A precise solution to the shortest path optimization problem in graphs using Z-numbers

<p>Communication networks are exposed to internal or external risks that can affect all or part of the system. The most important components that form the infrastructure of these systems are routers, which act as nodes. In the field of graph theory, there are sophisticated techniques that can be used to optimize the path of a packet as it travels through various routers from its origin to its destination. A notable example of such an algorithm is Dijkstra's algorithm, which is designed to efficiently determine the shortest path. The algorithm works under the assumption that the system operates under ideal conditions. Real-time systems can perform better if risk factors and optimal conditions are taken into account. The relationship between the nodes can be expressed by various metrics such as distance, delay, and bandwidth. The aforementioned metrics facilitate the calculation of the optimal path, with the ultimate objective of achieving low-latency networks characterized by rapid response times. Round-trip time (RTT) can be employed as a metric for measuring enhancements in a range of latency types, including those associated with processing, transmission, queuing, and propagation. The use of Z-numbers was employed in this study to incorporate risk into the optimal path metric. RTT was the preferred metric and reliability was represented by fuzzy linguistic qualifiers. A comparison of several scenarios was shown using a numerical example of a communication network. It is expected that this study will have a significant impact on the evolution from models that consider only ideal conditions to real-time systems that include risks using Z-numbers.</p>

Heterogeneous Multi-Robot (UAV-USV-AUV) Collaborative Exploration with Energy Replenishment

This work enables heterogeneous robot collaborative exploration using aerial (UAV), surface (USV), and underwater (AUV) vehicles. The approach employs dynamic programming to determine the optimal number of heterogeneous vehicles based on their characteristics and battery capacity of mobile charging stations. Collaborative Coverage Path Planning (CPP) execution, treated as a multi-traveling salesman problem, utilizes the Genetic Algorithm to get near-optimal solutions. Furthermore, Dijkstra's algorithm is integrated to address obstacles like islands and coral reefs, ensuring efficient energy replenishment for UAVs and AUVs. The strategy can be applied to short-term missions like rescue and delivery, as well as long-term operations like surveillance.

Shortest Route Navigation Indoors Using Digital Maps

Digital maps have revolutionized our ability to navigate to desired locations by providing the shortest routes, primarily in open spaces. However, this functionality is limited to outdoor environments. This study aims to extend this capability by enabling the determination of the shortest routes within indoor spaces. The research employs the Haversine method for distance measurement and integrates the Dijkstra algorithm for route determination. The findings demonstrate the feasibility of implementing the Haversine method and the Dijkstra algorithm for route determination within enclosed spaces. The research results show that searching for the shortest route indoors be done and the route can be displayed on a digital map. It was observed that the routing machine between route nodes did not perform optimally, prompting its replacement with a polyline.

Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

Research on Shortest Path Planning and Smoothing Without Obstacle Collision Based on Moving Carrier

In response to the challenges of path planning in complex scenarios, to overcome the influence of optimal path determination by the precision of grid map sizes, and to escape the uncertainty in solving by intelligent algorithms, this paper designs a method for obtaining an adjacency matrix based on node planning of shortest path diagrams with polygonal obstacles and then uses the Dijkstra algorithm to get the shortest path. For irregular curved obstacles, an edge straightening method is proposed. To enhance the applicability of the path, this paper introduces the constraint of minimum turning radius. It researches path smoothing under obstacle conditions based on arcs and straight lines, providing practical solutions for different scenarios. Considering the need to maintain a safe distance due to the size of the moving carrier and the deviation in trajectory tracking, this paper conducts an expansion analysis of obstacles. It obtains the trajectory on the arc after edge straight line fitting, followed by further smoothing treatment. The method proposed in this paper demonstrates excellent accuracy and robustness in path planning through simulation verification, proving its practicality and effectiveness in complex environments.

Reducing Search Regions for Fast Detection of Exact Point-to-Point Geodesic Paths on Meshes.

Fast detection of exact point-to-point geodesic paths on meshes is still challenging with existing methods. For this, we present a method to reduce the region to be investigated on the mesh for efficiency. It is by our observation that a mesh and its simplified one are very alike so that the geodesic path between two defined points on the mesh and the geodesic path between their corresponding two points on the simplified mesh are very near to each other in the 3D Euclidean space. Thus, with the geodesic path on the simplified mesh, we can generate a region on the original mesh that contains the geodesic path on the mesh, called the search region, by which existing methods can reduce the search scope in detecting geodesic paths, and so obtaining acceleration. We demonstrate the rationale behind our proposed method. Experimental results show that we can promote existing methods well, e.g., the global exact method VTP (vertex-oriented triangle propagation) can be sped up by even over 200 times when handling large meshes. Our search region can also speed up path initialization using the Dijkstra algorithm to promote local methods, e.g., obtaining an acceleration of at least two times in our tests.

System reliability evaluation of 12-pulse series converter station based on improved Dijkstra algorithm

System reliability evaluation of 12-pulse series converter station based on improved Dijkstra algorithm

On Finding Shortest Path Over Vocational High School in Yogyakarta Based on Graph Theory Algorithm

Finding shortest path is one of problem in graph theory. This research aims to apply the Dijkstra algorithm in determining the shortest route for State Vocational School (SMK) students throughout Yogyakarta. State Vocational Schools throughout Yogyakarta have several locations spread across various regions, and determining the shortest path is crucial for the efficiency of students' time and transportation. Dijkstra's algorithm was chosen because of its ability to find the shortest path in a weighted graph, which can be applied to complex networks of school trajectories. The research method involved collecting geographic data on schools, available transportation, and distance between locations. This data is used to build a weighted graph that represents the transportation network between State Vocational Schools throughout Yogyakarta. The implementation of Dijkstra's algorithm is then carried out using python programming language. We hope that the research results can provide mapping of the shortest routes between schools, minimize student travel time, and increase transportation efficiency. The practical implications of this research include the development of an information system or application that can help students, teachers and schools in planning daily trips. Apart from that, the research results can also be a basis for developing methods for determining the shortest route in the context of other schools or similar environments.

Penerapan Algoritma Dijkstra dalam Pencarian Rute Terpendek Wisata Religi

Pilgrimages to the graves of Waliyullah and the spreaders of Islam have become spiritual or religious tourism for Muslims in Java and even almost all Muslims in Indonesia. This opens up opportunities for travel agents to help pilgrims on religious tourism trips. One travel agency that offers religious tour packages is PT Mitra Jaya Group which operates in the Banyuwangi area, East Java. Many travel agencies are trying to increase their competitiveness, to survive competition between travel agencies, especially in improving services and financial management. One way that can be done is to determine the shortest route for religious tourism so that the distance traveled is minimal, where previously the route chosen was not based on proper research. In this paper, the search for the shortest route is carried out by applying the Dijkstra algorithm. The application of the Dijkstra algorithm can provide benefits regarding the shortest distance to religious tourism routes from PT Mitra Jaya Group. From the analysis results, the total shortest distance obtained was 2768.8 kilometers. The resulting shortest route distance is smaller than the distance before using the Dijkstra algorithm, namely 3005.6 kilometers. The difference is 236.8 kilometers and there is a distance saving of 7.87

Optimal management of full train load services in the shunting yard: A comprehensive study on Shunt-In Shunt-Out policies

A key aim of the European Union is to double freight rail traffic by 2050 in order to reduce pollution emissions and alleviate congestion by shifting traffic from roads to rail networks. To accomplish this objective, it is crucial to minimize emissions and high costs associated with shunting yard operations while maintaining an acceptable level of service. This research paper introduces a new MINLP model that optimizes the shunt-in and shunt-out (SISO) operations of wagons in a shunting yard that handles full train load services. Additionally, an efficient Multi-Objective Dijkstra Algorithm (MDA) is proposed to handle simultaneous shunt-out operations in a multi-train scenario. The MINLP model takes a mesoscopic approach, aiming to minimize the number of SISO operations while satisfying strategic and tactical objectives such as wagon fleet size, operational costs, and shunting locomotive emissions. Several versions of the mathematical model are described, each employing different Shunt-In (SI) policies with varying criteria for wagon selection and strong goal orientation. The Multi-Objective Dijkstra Algorithm determines the Multi-Objective Shortest Path between mandatory shunts, considering both the time required for shunting and clustering costs. It provides information on the clusters of wagons that need to be shunted out. To assess the effectiveness of the MINLP model, real train timetables for freight trains in the Bettembourg Eurohub Sud Terminal (Luxembourg) are used, and various KPIs related to tactical and strategic objectives are evaluated. Furthermore, the performance of the Multi-Objective Dijkstra Algorithm is compared to the Shunt-Out sub-model, considering average computation time and solution quality in relation to the MINLP model. The computational results demonstrate that the criteria for wagon selection have a significant impact on the analyzed KPIs.