SHORTEST PATH, ASSIGNMENT AND TRANSPORTATION PROBLEMS

1963 ◽  
Author(s):  
A. J. Hoffman ◽  
H. M. Markowitz
Keyword(s):  
Shortest Path ◽  
Transportation Problems ◽  
Path Assignment

Based on Transcad of the Shortest Path Assignment Method

2011 ◽  
Vol 219-220 ◽  
pp. 1105-1108
Author(s):  
Qing Yin Li ◽  
Rui Tang ◽  
Zhang Lu Tan
Keyword(s):  
Traffic Flow ◽  
Shortest Path ◽  
Road Traffic ◽  
Simulation Analysis ◽  
Dynamic Traffic Assignment ◽  
Urban Traffic ◽  
Assignment Method ◽  
Assignment Model ◽  
Path Assignment ◽  
Effective Path

Based on the Transcad all-or-nothing assignment model is a kind of static method; do not consider the travel time will be affected by traffic flow. The insufficient is that it does not conform to reality. In order to solve the all-or-nothing assignment model that to putting all of traffic flow on the shortest path, the text through the defining of the effective path and the traffic flow of the effective path to improve all-or-nothing assignment model. So the other road traffic flow can also assign and the results can reflect the assignment of urban traffic directly. It can be used to study the dynamic traffic assignment and traffic simulation analysis.

scholarly journals Implementation of Dijkstra Algorithm and Welch-Powell Algorithm for Optimal Solution of Campus Bus Transportation

2021 ◽  
Vol 7 (1) ◽  
pp. 31-40
Author(s):  
Nurwan Nurwan ◽  
Widya Eka Pranata ◽  
Muhamad Rezky Friesta Payu ◽  
Nisky Imansyah Yahya
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Optimal Program ◽  
Transportation Problems ◽  
Transportation Routes ◽  
Bus Transportation

This research deals with applying the Dijkstra algorithm and Welch-Powell algorithm to on-campus bus transportation problems. This research aims to determine the optimal solution of campus bus transportation routes in the shortest routes and schedules. In determining the fastest way, each intersection represented as a node, and the path described as the sides. The shortest path obtained    V1 - V2 - V5 - V8 - V9 - V10 - V13 - V16.  In determining the optimal schedule, the number of buses represents the vertices, and the time expresses the side that connects each node. The optimal program of the bus starts from 06.30 am to 5.00 pm. Every bus gets four sessions of departure and four sessions return with travel time each session is 60 minutes.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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