Open AccessItem Delivery Simulation Using Dijkstra Algorithm for Solving Traveling Salesman Problem
Author(s) -
Hagai Nuansa Ginting,
Andrew Brian Osmond,
Annisa Aditsania
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1201/1/012068
Subject(s) - dijkstra's algorithm , travelling salesman problem , computer science , shortest path problem , pathfinding , cluster analysis , suurballe's algorithm , a* search algorithm , mathematical optimization , floyd–warshall algorithm , algorithm , vehicle routing problem , process (computing) , routing (electronic design automation) , graph , mathematics , artificial intelligence , theoretical computer science , computer network , operating system
Companies that contribute to travel have many problems in the process of item delivery. Distances and priorities are considered for a process of item delivery based on the highest priority. A delivery target that can be done one day evidently exceed the expected limit and that is the impact. This is an example of the waste time and operational costs that should be at the same time that two or more addresses can be sent. Traveling Salesman Problem (TSP) was define a classical problem to finding the shortest route that salesman can be passed when visiting several places without visit again in the same place more than once. In this study, TSP requires all calculations of possible routes to be obtained. Then choose one of the shortest routes by prioritizing the things considered, namely distance and priority. Delivery is done quickly through the shortest route according to priority using the Dijkstra algorithm. Simulation shows that the Dijkstra algorithm must be approved by use clustering data for Dijkstra’s priorities and sub-routes to solve TSP problems. Simulation shows that the Dijkstra algorithm must be modified using Dijkstra’s priority clustering and sub-routing to solve TSP problems. The resulting route has an influence between two graphs. Complete graph has a distance efficiency of 47.8% and execution time of 48.1% compared to non-complete graphs.