A Tour Planning System Based on Solving the Traveling Salesman Problem Using the Taiwan Map

• Main Authors: Yu-Cheng Chen, 陳囿成
• Other Authors: Shih-Chieh Wei
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/81204406195555349889

碩士 === 淡江大學 === 資訊管理學系碩士班 === 95 === A tour planning system based on solving the traveling salesman problem is proposed. Given the per-day traveling times, categories of interest, and the range of scenic spots, the system can recommend a suitable tour plan for the user. The research divides into 3 parts. The first part is the evaluation of three approximation algorithms for solving the traveling salesman problem which include the genetic, the ant colony optimization and the simulated annealing algorithms. Selected cases from a standard test set are tested to find an algorithm which returns a result close to the optimal answer and less time-consuming. The second part is the evaluation of four shortest path web services which include the Dijkastra, the Euclidean A*, the directional landmark-based A*, and the undirectional landmark-based A* algorithms. A randomly generated test set is tested to find a web service which returns a result fast with least error. The third part is the tour planning itself which includes selection of scenic spots and hotels. To pick scenic spots for visit, our system provides a district mode and a range mode to satisfy different user needs. To pick hotels, our system tries to arrange a hotel both close in distance to the itinerary and in rank to the given grades of hotels. As result, the directional landmark-based A* web service is selected to calculate the shortest distance between two scenic spots. Also the simulated annealing algorithm is selected to arrange the visit order such that the total moving distance in order is approximate to the shortest one. According to the experiment result, the approximation error is tolerable. Thus the proposed tour planning system is suitable for independent travelers who want to arrange self-guided tours spanning several days on their own.