Research on Travel Route Planing Problems Based on Greedy Algorithm

TL;DR

This paper proposes a greedy algorithm-based route planning method that incorporates PCA, TOPSIS, and entropy weight evaluations to optimize tourist routes considering efficiency, time, and breaks.

Contribution

It introduces a novel combination of data reduction and evaluation techniques with a greedy algorithm for personalized tourist route planning.

Findings

Effective route optimization considering multiple factors.

Improved travel efficiency and reduced costs.

Personalized route customization based on tourist needs.

Abstract

The route planning problem based on the greedy algorithm represents a method of identifying the optimal or near-optimal route between a given start point and end point. In this paper, the PCA method is employed initially to downscale the city evaluation indexes, extract the key principal components, and then downscale the data using the KMO and TOPSIS algorithms, all of which are based on the MindSpore framework. Secondly, for the dataset that does not pass the KMO test, the entropy weight method and TOPSIS method will be employed for comprehensive evaluation. Finally, a route planning algorithm is proposed and optimised based on the greedy algorithm, which provides personalised route customisation according to the different needs of tourists. In addition, the local travelling efficiency, the time required to visit tourist attractions and the necessary daily breaks are considered in order to reduce the cost and avoid falling into the locally optimal solution.