Designing fuzzy expert system for creating and ranking of tourism scenarios using fuzzy AHP method

Abstract

One of the most important activities of tour and travel agencies is to select the appropriate tour configuration. There are normally two primary objectives of season and time period to set a group of cities called designing tour scenarios. The success of tour scenarios is deeply related to the experiments and wisdom of the experts and planners in travel agencies. This paper presents a fuzzy rule decision making to find the suitable set of cities where different possible criteria are ranked using analytical hierarchy procedure. The proposed model of this paper is applied for a real-world case study of Iranian tour agency and the results are analyzed under different circumstances.

Full text

* Corresponding author. Tel: +982177240541 Ext: 5004
E-mail addresses: amakui@iust.ac.ir (A. Makui),
© 2010 Growing Science Ltd. All rights reserved.
doi: 10.5267/j.msl..2010.01.007
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Management Science Letters 1 (2011) 29–40
Contents lists available at GrowingScience
Management Science Letters
homepage: www.GrowingScience.com/msl
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Designing fuzzy expert system for creating and ranking of tourism scenarios using fuzzy AHP
method
Zohreh Nikkhaha and Ahmad Makuib
aDepartment of Industrial Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran
bDepartment of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
A R T I C L E I N F O A B S T R A C T
Article history:
Received 25 June 2010
Received in revised form
14 October 2010
Accepted 14 October 2010
Available online
14 October 2010
One of the most important activities of tour and travel agencies is to select the appropriate tour
configuration. There are normally two primary objectives of season and time period to set a
group of cities called designing tour scenarios. The success of tour scenarios is deeply related to
the experiments and wisdom of the experts and planners in travel agencies. This paper presents
a fuzzy rule decision making to find the suitable set of cities where different possible criteria
are ranked using analytical hierarchy procedure. The proposed model of this paper is applied
for a real-world case study of Iranian tour agency and the results are analyzed under different
circumstances.
©2010GrowingScienceLtd.Allrightsreserved.
Keywords:
Analytical hierarchy process
Mamdani fuzzy method
Tourism Industry
Multiple criteria decision making
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1. Introduction
Travelling and tourism industry is considered the greatest and the most diverse industry in the world.
Most countries consider this dynamic industry as the main source of revenue, employment and
growth of their private sector and infrastructural development. Similar to other businesses, tourism
industry also includes numerous sub-sectors. One of these sub-sectors considered as the fastest
growing areas of tourism, must be declared as activities related to tour operating or tour planning
agency and the most important part is designing of tourism packages with the following two
objectives.
1. Designing tour scenarios
2. Combining the tour scenarios by various factors such as residency, means of transportation
and extra services.

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The primary focus of this research is on designing tour scenarios. Tour scenario involves the selection
of city and the priority of visiting the cities based on two inputs of the season of the year and the
duration of stay in the city. There are many cases where an expert determines the details of scenario
planning. This could lead to a feasible solution which may or may not be efficient practically. The
suitable configuration of cities could be also established using multiple criteria decision making
(MCDM) methods such as analytical hierarchy process (AHP). The implementation of AHP could
help us perform a pairwise comparison for all different choices and alternatives. The process is
normally repeated among various experts either in a team work or through a personal interview. We
may also use the concept of fuzzy programming whenever a precise decision cannot be extracted
directly. In this paper we propose a method which uses both AHP and fuzzy rules to determine the
best combination of a tour configuration. We plan to find all possible scenarios dynamically and the
best combinations of scenarios are chosen using AHP method.
One of the issues of a successful tourism is to measure the loyalty of customers to a particular agency.
Pearce and Kang (2009) performed a survey on some South Korean people who were visiting
Australia and determined some important factors affecting the customers' decision. Allen et al. (2009)
performed a survey on Australian people who were visiting different cities and determined two
important factors of income and accommodations prices for the frequency of their travelling pattern.
One of the important factors in tourism industry is the tour guide which is distributed among travelers
(Low et al, 1996). Zhang and Chow (2004) did a survey on tour guides based on 20 attributes and
found that 11 attributes helped travelers from China who were visiting Hong Kong pretty well.
Nationality is the other factor motivating factor for the people who travel to a particular country.
Kozak (2002) performed a pull and push study on some travelers who visited Mallorca and Turkey in
the summer of 1998. He analyzed his findings using some statistical tests such as Factor analysis and
found that some tourist motives differed between nationalities and place visited. The characterization
of hotel destination is another important factor affecting travelers' decision behavior. Ngai and Wat
(2003) developed a fuzzy decision making method for choosing suitable hotel and executed their
model on various hotels located in Hong Kong. Chou et al. (2008) developed a decision making
procedure to find a suitable hotel based on the distance from different places. Using a fuzzy decision
making method, they developed a decision support system to help find the suitable hotel. Chen et al.
(2010) used the concept of fuzzy programming to forecast the number of tourists who will come to a
country. Nikole and Francisco (2006) studied the relationship between the price and the distance that
affects hotel selection. This paper is organized as follows. We first develop the proposed model of the
paper in section 2. Section 3 is devoted to the implementation of the proposed fuzzy model. Finally
conclusion remarks are given at the end to summarize the contribution of the paper.
2. Definitions
The following tour scenarios exist.
• Tour scenarios with fixed center: In this travel package, tourist selects a fixed center and
constantly moves and visits around the center during the stay. In this scenario, tourist is not
benefited by the diversity of residency in different places.
• Circular tour scenario: In this travel package, tourist is benefited by residency in different
places and every time passes new passages.
• Tour scenario with open passage: In this plan tourist enters the destination in the target
country and moves there to visit different attractions in the cities and even neighboring
countries. However tourist does not return to a destination in order to get back to his home
country, but moves toward his home country from the last city or country visited. This sort of
tour scenario is called tour scenario with open passage.

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Assumptions
The following assumptions hold.
• The tour plan is designed for Iran.
• The interests and motivation of tourists for the trip is completely clear, and the motivation of
the tour members is cultural-historical.
• All tour members are foreigners and plan to stay in tour for 2 to 20 days
• The primary concern of all tourists is to visit historical monuments.
• Historical cities are limited to Isfahan, Tehran, Shiraz, Hamadan, Kermanshah, Kerman,
Ahvaz, Tabriz, Mashhad and Yazd.
• Selection of cities does not depend on number of people who form the tour.
• The tour package only serves the first time travelers.
• Circular type of tour scenario has been selected for the tourists.
3. The Proposed Methodology
In the proposed method of this paper we need to collect the necessary information from some experts.
Therefore we prepare some questionnaire so that we could gather the information using a face-to-face
interview. The survey must include the response of some technical and important questions from, at
least, five travel agencies. Table 1 summarizes the first set of the necessary information which
specifies the priority of the visiting cities.
Table1
The priority of visiting historical cities
Historical city Visiting priority
Isfahan 1
Shiraz 2
Yazd 3
Kerman 4
Tehran 5
Ahvaz 6
Hamadan 7
Mashhad 8
Kermanshah 9
Tabriz 10
Next, we need to specify the number of days we need to visit the cities. Table 2 summarizes the
information regarding the minimum number of days that tourists must stay in a city.

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Table 2
Minimum number of days
City Minimum number of days
Isfahan 3 days
Shiraz 2 days
Yazd 2 days
Tabriz 2 days
Hamadan 1 day
Mashhad 1 day
Kerman 2 days
Tehran 2 days
Ahvaz 1 day
Kermanshah 1 day
If Tehran is in a tour schedule it must be visited first. Also the cities are classified into three groups:
The first group includes Tehran, Isfahan, Shiraz, Kerman, Yazd and Ahvaz. The second group covers
Tehran, Hamadan, Kermanshah and Tabriz. The third one only covers Tehran and Mashhad. Note
that, since Tehran is the entering and exiting spot, it is repeated in all the tour packages. Since visiting
the sites between the cities are very important for visitors, any land travel is normally recommended
by customers.
3.1 Extracting fuzzy rules
As we explained earlier, there are two important factors in prioritizing the cities. The first one is the
amount of time we spend to stay in a city and the second one is the visiting season. Mamdani (1975)
is believed the first who used fuzzy logic to approximate reasoning using linguistic synthesis. We use
the same procedure as suggested by Mamdani to prioritize different rules. The analysis uses if and
then clause. On the if part, two inputs are used and on the then part ten outputs are considered and all
possible relationships are joint using the logical term of AND joint and 36 fuzzy if-then combinations
are designed. The next step is to classify each group in visiting sequence based on the priority rule of
visiting. In order to do this we need to indentify the necessary criteria in selecting the most suitable
scenario from the experts' point of view. Finally we need to prioritize the cities based on an AHP
method which has three different levels. In the first level, the primary objective is to find the most
appropriate tour scenario. The second level considers three criteria of visiting area quality, nationality
of tourist and profitability where the profitability criterion is divided into two sub-criteria of extra
service sale and direct sale of tour package. The third level considers the following three criteria,
1. Quality of transportation and residential facility
2. Level of access to welfare facility
3. Extra service quality
To perform pairwise comparison we use numbers from one to nine. Whenever we are unable to get a
number from expert we use fuzzy numbers given in Table 3.

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Table 3
Fuzzy words and corresponding numbers
Explanatory notes Logical term Numeral value
Extremely superior or more significant or more desirable Extreme 9
Preference or significance or very strong desirability Very strong 7
Preference or significance or strong desirability Strong 5
Moderately preferable or more significant or more desirable
Equal preference or significance or desirability Moderate 3
Preferences between the above distances 2,4,6,8
3.2 Inference fuzzy system construction
The Mamdani fuzzy controller system receives the input terms in terms of logical expression and
send the information into the fuzzy inference engine where inputs are defuzzified based on fuzzy rule
base. Fig. 1 demonstrates the details of the fuzzy logic interface.
Fig.1. A fuzzy system construction.
In the presented fuzzy system, Mamdani inference method has been used where the AND operation is
based on minimization and defuzzification and it is performed by Centeroid Method. The following
summarizes the necessary steps of our proposed method.
1. Inputs and outputs of the model: In the present research two inputs of Ttd which stands for
residency (number of days) and Season which stands for season of year have been used as inputs.
Residency variables changes from 2 to 20 days, and season variables includes four seasons of
year (spring, summer, autumn, winter). The output of the model also includes the names and the
circumstances of 10 historical cities based on three terms of bad, good and perfect for selection.
2. Attributing membership functions to input and output variables: The membership functions are
associated with two variables; number of days and season of year are defined as trapezoidal
function. The membership functions associated with the output variables of historical cities are
defined as generalized bell membership function. The collection of information is known as
database in the field of membership functions of fuzzy variables and domain of fuzzy variables.
The objective functions are defined based on the nature of the data and the application of fuzzy
toolbox.
3. Designing fuzzy rules base: As we already explained, 36 sets of if-then fuzzy rules are extracted
and each one of these if-then rules includes two inputs in prior sector and 10 outputs in the
following sector.
4. Combining outputs and forming a single fuzzy set: Since the decisions are based on testing all the
rules in fuzzy inference system, it is necessary to combine the rules to make decision.
Combination is a process in which all the extracted fuzzy sets from each rule are combined
together and produce a single fuzzy set.
Fuzzy Rule Based conditions
Fuzzifier Defuzzifier
Fuzzy Inference Engine
X in U Y in V
Fuzzy sets in U
Fuzzy sets in V

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5. Defuzzication: output of model from the previous step is a fuzzy set and it is necessary that
the resulted fuzzy value is transformed to a determined spot as the best choice of output fuzzy
set.
If the stay duration in Iran is equal to13 days, therefore Ttd=13 and this means we are in T14 and
season of year is equal to 0.494, by referring to membership functions base, it is observed that this
numeric value belongs to both fall and summer. In other words, there is not much difference between
late summer and early autumn from climatic point of view, therefore the membership function about
the number of days and season of year and historical cities condition are shown in Fig. 2.
Fig. 2. (a) Number of days membership function Fig. 2. (b) Season of the year membership function
Fig. 2. (c) Condition of cities membership function
The rules in fuzzy system have been extracted using expert people (See Appendix A). Table 4
summarizes the defuzzified values associated with each city.
Table 4
Defuzzified values per historical city
City value City Value
Isfahan 0.6750 Kerman 0.50
Shiraz 0.6750 Yazd 0.6750
Tehran 0.6750 Ahvaz 0.50
Hamadan 0.6480 Tabriz 0.6480
Mashhad 0.6750 Kermanshah 0.6480
Table 5 also shows the details of degree of dependency to three logical words of good, bad and
perfect.

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Table 5
Degree of dependency to bad,good and perfect set
City Defuzzified values
Degree of
dependency to good
set
Degree of
dependency to bad
set
Degree of
dependency to
perfect set
Isfahan 0.6750 0.85 0.0 0.15
Shiraz 0.6750 0.85 0.0 0.15
Tehran 0.6750 0.85 0.0 0.15
Hamadan 0.6480 1 0.0 0.9
Mashhad 0.6750 0.85 0.0 0.15
Kerman 0.5 1 0.0 0
Yazd 0.675 0.85 0.0 0.15
Ahvaz 0.5 1 0.0 0
Tabriz 0.6480 0.1 0.0 0.9
Kermanshah 0.6480 0.1 0.0 0.9
Fig. 3 shows the roadmap of fuzzy inference process. Twelve small plots (two yellow plots and ten
blue plots) across the top of the figure represent the antecedent and consequent of the first rule. Each
rule is a row of plots, and each column is a variable. The first two columns of plots (the eight yellow
plots) show the membership functions referenced by the antecedent, or the if-part of each rule. The
forty blue plots show the membership functions referenced by the consequent, or the then-part of
each rule. The fifth plot in each column of plots represents the aggregate weighted decision for the
given inference system. This decision will depend on the input values for the system.
Fig.3. Roadmap of fuzzy inference process
The relationship between the membership function of two inputs and one outputs generate three-
dimensional plots shown in Fig.4. In this figure the relationships between two inputs and one outputs:
residential time and seasons of the year and the output of selected historical cities' circumstance can
be observed, simultaneously. It shows the circumstance of Shiraz city considering any number of
days and season of year.

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Fig.4. Mapping the inputs to output for the city of Shiraz
As we explained earlier, there are three groups of cities and since Tehran must be included in all three
groups we have,
1. Tehran-Shiraz- Isfahan- Kerman- Yazd- Ahvaz
2. Tehran- Hamadan- Tabriz- Kermanshah
3. Tehran- Mashhad
It is also desired that all tourists travel from the first city to the last one by airplane and from the last
city they are returned to the nearest one by car and the process continues using busses till all
passengers come back to the origin city. The following is the summary of three groups,
1. Tehran- Ahvaz- Shiraz- Kerman- Yazd- Isfahan
2. Tehran- Tabriz- Hamadan- Kermanshah
3. Tehran- Mashhad
Evaluation of extracted scenarios using hierarchical model of AHP(see Fig.5).

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Fig.5.The analytical hierarchical process
The final weights of paired comparison matrices in three presented scenarios:
A1: Tehran- Mashhad
A2: Tehran- Ahvaz- Shiraz- Kerman- Yazd- Isfahan
A3: Tehran- Tabriz- Hamadan- Kermanshah
Table 6
The final weights extracted from AHP analysis for three attributes
Criterion weight
Quality of residential facilities and transportation 0.6321
Nationality of tourist 0.2333
Profitability 0.1232

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Table 7
The final weights related to paired comparison matrices of relationships between sub-criteria
Sub-criterion weight
Quality of residential facilities and transportation 0.4551
Access amount to welfare facilities 0.0544
Quality of extra services 0.1296
Direct sale of tour package 0.0222
Extra service sale 0.0940
The results obtained from the analysis of data can be classified as follows:
1. Results obtained from developing Mamdani fuzzy system indicate that as the red index line
in output of model and in input of number of days moves toward right, the number of days
tends to increase and the selection of cities with high historical attractions tends to be the
best for selection. Also when we consider the season of the year as input we realize that as
the red index line moves between the span of spring and summer, selection of significant
historical cities from the viewpoint of historical attraction, residential attraction and
transportation tends toward good and the best circumstance for selection.
2. The final evaluation of the criteria has been performed by AHP fuzzy model and the results
A1 is Tehran- Mashhad, A2 is Tehran- Ahvaz- Shiraz- Kerman- Yazd- Isfahan and A3
is Tehran- Tabriz- Hamadan- Kermanshah. Also the fuzzy utilities preference score are as
follows µs(1) = 0.15, µs(2) = 1.0 and µs(3) = 0.9.Therefore, A2 utility tour scenario is the
first preference and A3 tour scenario is the second and A1 tour scenario is the third. Fig.6
shows the results of the ranking fuzzy utility scenarios.
Fig.6. results of ranking fuzzy utility scenarios A1, A2, A3

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4. Conclusion
In this paper, we have presented a fuzzy decision making methodology to find the best scenario
planning of an Iranian tourist industry. We have considered two primary criteria for designing a
methodology which were the number residential and the season of the year. The proposed model of
this paper has considered AHP method to prioritize different criteria and using fuzzy numbers we
have used Mamdani method to analyze the proposed approach for an empirical study of Iranian case
study. The results are also analyzed under various circumstances.
Acknowledgment
We would like to appreciate the honorary referees' precious opinions and guidance which have had a
great influence in improving the article.
Appendix A - fuzzy rules for the time of 13 days and season of year equal to 0.494.
1. If(Ttd is T14) and (Season is Spring) then (Isfahan is Perfect)(Shiraz is Perfect)(Yazd is
Perfect)(Kerman is Perfect)(Tehran is Perfect)(Ahwaz is Perfect)(Hamedan is
Good)(Kermanshah is Good)(Tabriz is Good)(Mashahd is Perfect)
2. If(Ttd is T14) and (Season is Summer) then (Isfahan is Perfect)(Shiraz is Perfect)(Yazd is
Perfect)(Kerman is Good)(Tehran is Perfect)(Ahwaz is Good)(Hamedan is
Perfect)(Kermanshah is Perefect)(Tabriz is Perefect)(Mashahd is Perfect)
3. If(Ttd is T14) and (Season is full) then (Isfahan is Good)(Shiraz is Good)(Yazd is
Good)(Kerman is Good)(Tehran is Good)(Ahwaz is Good)(Hamedan is Bad)(Kermanshah is
Bad)(Tabriz is Bad)(Mashahd is Good)
4. If(Ttd is T14) and (Season is Winter) then (Isfahan is Good)(Shiraz is Good)(Yazd is
Bad)(Kerman is Bad)(Tehran is Good)(Ahwaz is Perfect)(Hamedan is Bad)(Kermanshah is
Bad)(Tabriz is Bad)(Mashahd is Bad)
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