Data-Driven travel itinerary with branch and bound algorithm

In this paper, we study a novel self-driving travel planning problem, where the tourist aims to minimize the total cost. The idea is to use a mathematical model to planning a route-Time scheme for travel spots and hotels. Specifically, this planning determines the tour for travel spots and considers the hotel selection under the rest break constraint, as well as schemes routes and time arrangement for the trip. Mean-while, based on real-Time and multi-resource demand, we use multi-resource data to execute multiple websites' information extraction. We utilize two algorithms to solve the proposed problem and make a comparison, one is exact branch and bound scheme and the other is the branch and bound based heuristic algorithm. In the proposed heuristic algorithm, the travel spots in the problem are decomposed by K-means algorithm, then each group of travel spots is bounded by the greedy algorithm and Hungarian method for upper bound and lower bound, respectively. Each branch node branches using Hungarian method and each branch can be treated as an assignment problem solved by Hungarian method. Finally, we give numerical examples and discuss the results.