Tourism Product Design and Development based on Heuristic Algorithm

Ye Hong Tourism and Air Service, Hunan International Business Vocational College, Changsha 410006, China )

yehong2015@sina.com

Abstract—Tourism product traffic route optimization directly affects income and development of the enterprise. In order to overcome the disadvantage that ant colony algorithm is easy to fall into local optimal solution, the heuristic factor and information volatilization coefficient adopts the principle of dynamic setting to increase the diversity of the path selection. Then the improved heuristic algorithm is used in tourism product route optimization design. The improved ant colony algorithm is used in tourism transportation route optimization design, which reduces difficulty in solving complex optimization problem and the improved ant colony algorithm has better result.

Keywords-tourism product design; heuristic algorithm; ant colony algorithm

I. INTRODUCTION At present, the development of tourism industry is still at

the primary stage in our country, and the travel agency plays the role of tourism traffic organizer[1,2]. Its main task is to carry out the tourism transportation, accommodation and attractions. Of course, tourism product is determined by the traffic conditions and traffic routes. As a result, tourism traffic route optimization will directly affect the enterprise customers, income and development[3,4].

Ant colony algorithm[5-8] is selected for tourist product traffic route optimization problem. In the aspect of algorithm improvement, the pseudo-random proportion rule is used for ant routing selection. The heuristic factor and information volatilization coefficient adopts the principle of dynamic setting to increase the diversity of the path selection, which improves the disadvantage that ant colony algorithm is easy to fall into local optimal solution. Finally, the improved ant colony algorithm is used in tourism transportation route optimization design, which reduce difficulty in solving complex optimization problem.

In the next section, we introduce mathematical description of the tourism product optimization problem. In Section 3, tourism design based on heuristic algorithm[9-12] is proposed. In Section 4, we do empirical research based on improved ant colony algorithm. In Section 5 we conclude the paper and give some remarks.

II. THE MATHEMATICAL DESCRIPTION OF THE TOURISM PRODUCT OPTIMIZATION PROBLEM

The assumption is a travel agency design-three day tour line. Travel agency chooses 14 number of scenic spots according to the needs of tourists themselves and attractions of tourism resources, as shown in the figure 1. A represents the accommodation for the tourists. Each day, the tourists

start from point A, after visiting some scenic spots, they go back to point A for a rest. ijw represents weight value of optimal route between two scenic spots.

Figure 1. product of route optimization

There are n number of scenic spots. There exsits route between any two spots and there exsits route between each spot and restaurant 0v . The node label is 1v , 2v , … . The travel time is m days. In m days, each spot is visited once and all spots are visited. The mathematical model is as follows.

1 0 1

n n m

ijk iji j k

MinZ x w= = =

=

St: 0 0 1

9n n n

ki ijk ij ki ii j i

y x t y t= = =

+ , 0 0t =

11

m

kiky

=

=

0kiy = or 1, 1,2,3, ,i n= ; 1,2,3, ,k m=

1ijk ki

jx y

=

=

0 01 1

n k

i k ki k

x y k= =

= =

ijkx is 1 or 0.

The travel time of each spot is it , and travel time of spot

to spot j is ijt . The visiting time of each tourist each day is