Analisis Perbandingan Algoritma Djikstra, A-Star, dan Floyd Warshall dalam Pencarian Rute Terdekat pada Objek Wisata Kabupaten Dompu

Diera industri 4.0, penggunaan peta tidak lagi berbentuk lembaran ataupun buku. Kini terdapat sebuah layananan peta digital yaitu platform Leafleat.js, yang memudahkan penggunanya untuk mendapatkan informasi rute dari objek ke objek lainnya dan mencari lokasi hampir diseluruh dunia. Pada penelitian ini menggunakan objek yang real yaitu menampilkan lokasi sebenarnya menggunakan platform Leaflet.js dan parameter yang berbeda, dari hal tersebut penelitian ini akan membandingkan kinerja dari Algoritma Dijkstra, A* dan Floyd Warshall untuk menentukan waktu proses pencarian rute terdekat dari objek wisata ke objek wisata lain menggunakan bahasa pemograman PHP. Hasil pengujian program didapatkan jarak dan rute yang sama serta rata-rata waktu proses program yang berbeda. Waktu proses algoritma Dijkstra sebesar 0,0060 detik, algoritma A* sebesar 0,0067 dan algoritma Floyd Warshall sebesar 0,0433 detik. Berdasarkan hasil tersebut bahwa algoritma  Dijkstra lebih unggul dalam proses pencarian rute. AbstractIn the industrial era 4.0, the use of maps is no longer made of book sheets. Now a digital map service is available, the Leafleat.js platform, which provides users to get route information from other attractions and find locations that have been saved by the world. In this study using real objects that display the actual location using the Leaflet.js platform and different parameters, from this study will compare the performance of the Dijkstra, A * and Floyd Warshall Algorithms for the process of finding other tourist information using the PHP programming language. The results of testing the program obtained the same distance and route with different program processing time. Dijkstra algorithm processing time is 0.0060 seconds, A* algorithm is 0.0067 and Floyd Warshall algorithm is 0.0433 seconds. Based on these results, Dijkstra is superior in the route search process

Application of A* algorithm in intelligent vehicle path planning

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra’s algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles

An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop

This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature