scholarly journals Dijkstra Algorithm for Determining the Shortest Path in the Case of Seven Hotels in Manado City Towards Manado’s Sam Ratulangi Airport.

d'CARTESIAN ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 158
Author(s):  
Yohana Permata Hutapea ◽  
Chriestie E.J.C. Montolalu ◽  
Hanny A.H. Komalig
Keyword(s):  
Graph Theory ◽  
Travel Time ◽  
Shortest Path ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Google Maps ◽  
Transportation Mode ◽  
Dijkstra's Algorithm ◽  
The Difference

Manado city has many notable tourist sites, resulting in the increase of the number of tourists visiting every year. Tourists require hotels with adequate facilities for their stay, such as 4-star hotels. After visiting Manado, tourists go back to where they come from. One of the transportation mode being used is airplanes. They then need a path to go through and not the usual one; they need the shortest path to get to Sam Ratulangi airport. Based on previous research, the shortest path is modeled by Graph Theory. Hotels will be represented as vertices, and the path from each hotels and to the airport will be represented as edges. The shortest path are searched by using Dijkstra’s Algorithm then will see the difference to shortest path from google maps. Based on the analysis results, Dijkstra’s Algorithm selects the shortest path with the smallest weight. The difference between Dijkstra’s Algorithm and google maps can be concluded that, in determining the shortest path used for the trip from the 4-star hotel to the airport, Dijkstra’s Algorithm is emphasized towards short travel distance, whereas google maps is emphasized more in short travel time.

scholarly journals Finding Your Way: Shortest Paths on Networks

2020 ◽  
Author(s):  
Teresa Rexin ◽  
Mason A. Porter
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real World ◽  
Shortest Paths ◽  
Travel Distance ◽  
Dijkstra’S Algorithm ◽  
Total Cost ◽  
The Real ◽  
Dijkstra's Algorithm ◽  
The Cost

Traveling to different destinations is a big part of our lives. How do we know the best way to navigate from one place to another? Perhaps we could test all of the different ways of traveling between two places, but another method is using mathematics and computation to find a shortest path. We discuss how to find a shortest path and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance or travel time. We also discuss how shortest paths can be used in the real world to save time and increase traveling efficiency.

scholarly journals The Implementation of Kruskal’s Algorithm for Minimum Spanning Tree in a Graph

2021 ◽  
Vol 348 ◽  
pp. 01001
Author(s):  
Paryati ◽  
Krit Salahddine
Keyword(s):  
Shortest Path ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Application System ◽  
Path Creation ◽  
Dijkstra's Algorithm

Kruskal’s Algorithm is an algorithm used to find the minimum spanning tree in graphical connectivity that provides the option to continue processing the least-weighted margins. In the Kruskal algorithm, ordering the weight of the ribs makes it easy to find the shortest path. This algorithm is independent in nature which will facilitate and improve path creation. Based on the results of the application system trials that have been carried out in testing and comparisons between the Kruskal algorithm and the Dijkstra algorithm, the following conclusions can be drawn: that a strength that is the existence of weight sorting will facilitate the search for the shortest path. And considering the characteristics of Kruskal’s independent algorithm, it will facilitate and improve the formation of the path. The weakness of the Kruskal algorithm is that if the number of nodes is very large, it will be slower than Dijkstra’s algorithm because it has to sort thousands of vertices first, then form a path.

scholarly journals Z-Dijkstra’s Algorithm to solve Shortest Path Problem in a Z-Graph

2017 ◽  
Vol 10 (1) ◽  
pp. 180-186
Author(s):  
Siddhartha Biswas
Keyword(s):  
Graph Theory ◽  
Shortest Path ◽  
Weighted Graph ◽  
Shortest Path Problem ◽  
Dijkstra’S Algorithm ◽  
Dijkstra's Algorithm

In this paper the author introduces the notion of Z-weighted graph or Z-graph in Graph Theory, considers the Shortest Path Problem (SPP) in a Z-graph. The classical Dijkstra’s algorithm to find the shortest path in graphs is not applicable to Z-graphs. Consequently the author proposes a new algorithm called by Z-Dijkstra's Algorithm with the philosophy of the classical Dijkstra's Algorithm to solve the SPP in a Z-graph.

scholarly journals An Improved Route-Finding Algorithm Using Ubiquitous Ontology-Based Experiences Modeling

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Maryam Barzegar ◽  
Abolghasem Sadeghi-Niaraki ◽  
Maryam Shakeri ◽  
Soo-Mi Choi
Keyword(s):  
Travel Time ◽  
Critical Issue ◽  
Dijkstra’S Algorithm ◽  
Geospatial Information ◽  
New Employees ◽  
Dijkstra's Algorithm ◽  
Route Finding ◽  
Route Length ◽  
Long Time ◽  
The Difference

Every day, people are hired in different organizations and old and retiring employees are eliminated from enterprise systems. Eliminating these individuals from organizations leads to the loss of their spatial experiences. In addition, since new employees lack relevant experience, they need a long time to develop the correct skills for the company and may even cause damage to the organization during this learning process. Therefore, storing the spatial experience of individuals is a critical issue. Due to the intelligence of ubiquitous Geospatial Information System (GIS), any experience from any user can be received and stored. In the future, based on these experiences, an appropriate service to each user may be provided as needed. This paper aims to propose an ontology-based model to store spatial experiences in the field of ubiquitous GIS route finding. For this purpose, first ontology is designed for route finding, and then according to this ontology, an ontology-based route-finding algorithm is developed for ubiquitous GIS. Finally, this algorithm is implemented for Tehran, Iran, and its results are compared with the shortest path algorithm (Dijkstra’s algorithm) in terms of the route length and travel time for peak traffic time. The results show that while the route length obtained from the ontology-based algorithm is more than Dijkstra’s algorithm, the travel time is lower, and on some routes the difference in travel time saved reaches 35 minutes.

Time-dependent Dijkstra’s algorithm under bipolar neutrosophic fuzzy environment

2021 ◽  
pp. 1-10
Author(s):  
Esra Çakır ◽  
Ziya Ulukan ◽  
Tankut Acarman
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Real Life ◽  
Time Dependent ◽  
Dijkstra’S Algorithm ◽  
Fuzzy Environment ◽  
Dijkstra's Algorithm ◽  
Fuzzy Graphs ◽  
Negative Statements ◽  
Shortest Path Algorithms

Determining the shortest path and calculating the shortest travel time of a complex networks are important for transportation problems. Numerous approaches has been developed to search shortest path on graphs, and one of the well-known is the Dijkstra’s label correcting algorithm. Dijkstra’s approach is capable of determining shortest path of directed or undirected graph with non-negative weighted arcs. To handle with uncertainty in real-life, the Dijkstra’s algorithm should be adapted to fuzzy environment. The weight of arc -which is the vague travel time between two nodes- can be expressed in bipolar neutrosophic fuzzy sets containing positive and negative statements. In addition, the weights of arcs in bipolar neutrosophic fuzzy graphs can be affected by time. This study proposes the extended Dijkstra’s algorithm to search the shortest path and calculate the shortest travel time on a single source time-dependent network of bipolar neutrosophic fuzzy weighted arcs. The proposed approach is illustrated, and the results demonstrate the validity of the extended algorithm. This article is intended to guide future shortest path algorithms on time-dependent fuzzy graphs.

scholarly journals The Implementation of Kruskal’s Algorithm for Minimum Spanning Tree in a Graph

2021 ◽  
Vol 297 ◽  
pp. 01062
Author(s):  
Paryati ◽  
Krit Salahddine
Keyword(s):  
Shortest Path ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Application System ◽  
Path Creation ◽  
Dijkstra's Algorithm

Kruskal’s Algorithm is an algorithm used to find the minimum spanning tree in graphical connectivity that provides the option to continue processing the least-weighted margins. In the Kruskal algorithm, ordering the weight of the ribs makes it easy to find the shortest path. This algorithm is independent in nature which will facilitate and improve path creation. Based on the results of the application system trials that have been carried out in testing and comparisons between the Kruskal algorithm and the Dijkstra algorithm, the following conclusions can be drawn: that a strength that is the existence of weight sorting will facilitate the search for the shortest path. And considering the characteristics of Kruskal’s independent algorithm, it will facilitate and improve the formation of the path. The weakness of the Kruskal algorithm is that if the number of nodes is very large, it will be slower than Dijkstra’s algorithm because it has to sort thousands of vertices first, then form a path.

scholarly journals Optimization of Shortest Path Problem using Dijkstra’s Algorithm in Imprecise Environment

2021 ◽  
Vol 10 (10) ◽  
pp. 216-221
Author(s):  
Samir Dey ◽  
Sriza Malakar ◽  
Shibnath Rajak
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Numerical Example ◽  
Network Problem ◽  
Dijkstra's Algorithm

Dijkstra algorithm is a widely used algorithm to find the shortest path between two specified nodes in a network problem. In this paper, a generalized fuzzy Dijkstra algorithm is proposed to find the shortest path using a new parameterized defuzzification method. Here, we address most important issue like the decision maker’s choice. A numerical example is used to illustrate the efficiency of the proposed algorithm.

scholarly journals Journey to Crime Using Dijkstra’s Algorithm

2017 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
J. O. Olusina ◽  
J. B. Olaleye
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Shortest Path ◽  
Geographic Information ◽  
Service Area ◽  
Dijkstra’S Algorithm ◽  
Crime Mapping ◽  
Cost Matrix ◽  
Dijkstra's Algorithm ◽  
Journey To Crime

This paper describes some benefits of crime mapping in a Geographic Information Systems (G.I.S.) environment. The underlining principle of Journey to Crime was discussed. Crime Spots and Police Stations in the study area were mapped, Shortest-Path, Closest Facility, Service Area and OD (Origin – Destination) Cost Matrix were determined based on Dijkstra's Algorithm. Results show that the distribution of police stations does not correspond with the spread of crime spots.

Sign in / Sign up

Export Citation Format

Share Document