Compétitivité Destinations Neige MCDA CAEPEM ITEM

04/02/2014

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Dr. BOTTI Laurent Dr. GONCALVES OlgaRAKOTONDRAMARO Hanitra

Perpignan University / CAEPEM

1st International Winter University

29th, 30th and 31st January 2014 , LABEX Item

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1. Introduction

2. Multi-criteria Methods

3. The ski resorts competitiveness

4. Conclusion

The competitiveness of French ski

resorts : Multi-criteria approach

1. Introduction



4. Conclusion

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Tourism destinations are the central elements of the tourism

system

Tourists consider overall destinations when deciding where to

vacation. It maybe:

– Country (Omerzel Gomezel & Mihalic, 2008),

– Regions (Cracolici & Nijkamp, 2008)

– City (Clavers-Cortés et al, 2007)

– Or type of tourism (Melian-Gonzalez et Garcia-Falcon, 2003)

1. Introduction



4. Conclusion

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There is competitiveness between a multiplicity of actors

implicated in the tourism experience

This competitiveness is relative (Dwyer et al. ,2011)

Helene Michel and Gabriel Guallino give some information about

the competitiveness of sky resorts in France

(http://www.lexpress.fr/palmares/ski/default.asp)

This paper aims to compare this competitiveness with considering

the tourist profiles: Real ski enthusiastic, family, snowboarder and

freerider, cross-country skier.

1. Introduction



4. Conclusion

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Why are MCDA methods relevant to deal with

competitiveness of sky resort destination ?

– MCDA for methods providing quantitative approach to support

decision making in problems involving several criteria and

choices (alternatives or actions) (Figueira, Mousseau & Roy,

2005)

1. Introduction



4. Conclusion

Step of decision making process:

04/02/2014

65. Select one destination from a set of n alternatives possibles

4. Evaluation of alternatives under each criteria

3. List the Criteria

The ski area The quality of

snow The budget

Extreme activities

Events

2. Identification of the alternatives

Ski resorts destinations in France (Alpes, Massif Central, Jura, Vosges et Pyrénées)

1. Tourist determines his profile

Real ski enthusiastic

FamilySnowboarder and

freeriderCross-Country

skier

1. Introduction



4. Conclusion

Although MCDA methods can be applied to different areas, the

litterature is quite narrow when considering the tourism field

– TOPSIS was used by Zhang et al. (2011) to rank 16 cities in China

– TOPSIS, PROMETHEE and the WSM was used by Ishizaka, Nemery and

Lidouh (2013) to select the location of a casino in London

– ELECTRE I was used by Botti and Peypoch (2013) to choose the best

destination in Hawaï

– ELECTRE II was used by Andrades-Caldito et al. (2013) to rank provinces

of Andalusia (Spain)

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1. Introduction



4. Conclusion

Here, we try to rank a set of alternatives (ski resorts) by tourists profils. So, we use ELECTRE III (Elimination et Choix Traduisant la Réalité ).

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Concordance Index

Disconcordance

Index

Credibility matrix

Descending preorder Ascending Preorder

Final ranking

Veto ? Yes

No

Construction of

the outranking

relations

Exploitation of

the outranking

relations

1. Introduction



4. Conclusion

For each criterion, thresolds and criteria weights are determined by user

– Pi : weight of the criterion i

– n: number of criteria

– qi: indifference threshold for the criterion i

– pi: preference threshold of the alternative on the criterion i

To construct the outranking relations, we determine:

– The concordance indices to indicate the truthfulness of assertation “destination di

outranks destination dk” (di S d)

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m

j

j

m

j

kijj

ik

P

ddcP

C

1

1

),(*

Performance de la destination

k moins performance de la

destination i sur le critère j

qj pj

Crédibilité de la

concordance pour

« i surclasse k »

0

1

cj(di,dk)

gj(dk)-gj(di)

1. Introduction



4. Conclusion

– The discordance index is cautious to refuse the assertation

“destination di outranks destination dk” (dj(di,dk))

Compare gj(dk)-gj(di) with preference theresold pj and veto theresold vj

– We combine the concordance and discordance indices to obtain the

degree of credibility. It indicates if the outranking hypothesis is true

and are gathered in a credibility matrix.

0<δik<104/02/2014

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Fj ik

kij

ikikC

dddC

1

),(1*

Avec ikkij CdddFjjF ),(,/ et FF

1. Introduction



4. Conclusion

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To illustrate the ranking process of ELECTRE III, we use in

following example with 59 ski resorts and 5 criteria

Alternatives SKY RESORTS Ski areaQuality of

snowBudget

Extreme

activitiesEvents

1 A0001 LA PLAGNE 15.5 18 10 15.38 18

2 A0002 LES ARCS BOURG ST MAURICE 17.75 18 4 16.92 14

3 A0003 COURCHEVEL 19 18 4 13.85 17

4 A0004 VAL THORENS 16.75 20 4 10.77 16.5

5 A0005 LES MENUIRES 14.5 18 2 7.69 15

6 A0006 TIGNES 16.75 20 4 13.85 12

7 A0007 ALPE D'HUEZ 15.5 20 6 13.85 13

8 A0008 VAL D'ISERE 16.75 20 4 10.77 17

9 A0009 LES 2 ALPES 14.5 20 10 12.31 16

10 A0010 SERRE CHEVALIER 14 16 8 12.31 14

11 A0011 MERIBEL (LES ALLUES) 17 16 4 13.85 16

12 A0012 AVORIAZ 1800 14.75 14 4 10.77 10

13 A0013 FLAINE (GRAND MASSIF) 12 14 4 9.23 6

14 A0014 CHATEL 12.25 6 8 7.69 10

15 A0015 MEGEVE 16.5 16 6 13.85 15

16 A0016 LA CLUSAZ 13.5 16 4 15.38 14

17 A0017 LES GETS 16.5 12 6 9.23 10

18 A0018 La Mongie (Domaine du Tourmalet) 9.75 14 4 6.15 10

19 A0019 MORZINE 12.75 10 8 15.38 6

20 A0020 LES SAISIES 12.25 14 8 10.77 15

21 A0021 MONTGENEVRE 15.25 18 8 7.69 6

22 A0022 VALMOREL 11.25 12 4 10.77 6

23 A0023 VARS 14.25 16 10 13.85 7

performance

1. Introduction



4. Conclusion

We attribute weight for each profile

Where for each criterion

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𝑤𝑖 = 1

5

𝑖=1

Profile Ski area Quality of

snow

Budget Extreme

activities

Events

Real ski

enthusiastics

30% 20% 20% 15% 15%

Family 20% 20% 30% 15% 15%

Snowboarder

and freerider 15% 30% 15% 25% 15%

Cross-

country skier 20% 40% 20% 10% 10%

1. Introduction



4. Conclusion

We define the preference, indifference and veto thresolds by criterion for each profile

q= 0,2; p=3

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Profile Thresolds Ski area Quality

of snow Budget

Extreme

activities Events

q

p

v

q

p

v

q

p

v

q

p

v

Real ski

enthusiastics

Snowboarder

and

freerider

Cross-

country skier

Family

1. Introduction



4. Conclusion

Now, alternatives (sky resorts) are pairewise compared (di,dk)

With i=59 the total number of alternatives04/02/2014

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d1 d2 d3 d4 d5 d6 … di

d1 - d1,d2 d1,d3 d1,d4 d1,d5 d1,d6 … d1,di

d2 d2,d1 - d2,d3 d2,d4 d2,d5 d2,d6 … d2,di

d3 d3,d1 d3,d2 - d3,d4 d3,d5 d3,d6 … d3,di

d4 d4,d1 d4,d2 d4,d3 - d4,d5 d4,d6 … d4,di

d5 d5,d1 d5,d2 d5,d3 d5,d4 - d5,d6 … d5,di

d6 d6,d1 d6,d2 d6,d3 d6,d4 d6,d5 - … d6,di

… -

di di,d1 di,d2 di,d3 di,d4 di,d5 di,d6 … -

Outranking relation

1. Introduction



4. Conclusion

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Distillation procedures must be used to rank the alternatives:

– A descending distillation: select the best rated alternatives initially and

finishing with the worst

– An ascending distillation: the worst rated alternatives are selected first and

the distillation terminates with the assignement of the best alternatives

The combination of the two pre-ranking gives the final ranking

1. Introduction



4. Conclusion

Rank of top 15 by profile : ELECTRE III vs WSM

– Real ski enthusiastic

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Ranking Sky resort

1st LA PLAGNE

2nd COURCHEVEL

3rd LES 2 ALPES

4th LES ARCS BOURG ST MAURICE

5th VAL D'ISERE

6th VAL THORENS

7th ALPE D'HUEZ

8th TIGNES

9th MEGEVE

10th MERIBEL (LES ALLUES)

11th LES 7 LAUX

12th LA TOUSSUIRE

13th SERRE CHEVALIER

14th VARS

15th LA CLUSAZ

1. Introduction



4. Conclusion

Family

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1st • LA PLAGNE

2nd

•COURCHEVEL

• LES 2 ALPES

• LES 7 LAUX

3rd• LA TOUSSUIRE

4th

• LES ARCS BOURG ST MAURICE

•VARS

• SAINT-LEGER-LES-MELEZES

5th

•ALPE D'HUEZ

•MERIBEL (LES ALLUES)

•MEGEVE

6th•VAL THORENS

•VAL D'ISERE

•SERRE CHEVALIER

7th

• TIGNES

• LA ROSIERE

•QUEYRAS (CEILLAC-EN-QUEYRAS)

Ranking Sky resort

1st LA PLAGNE

2nd LES 2 ALPES

3rd LES 7 LAUX

4th COURCHEVEL

5th LA TOUSSUIRE

6th LES ARCS BOURG ST MAURICE

7th ALPE D'HUEZ

8th VAL D'ISERE

9th VAL THORENS

10th MEGEVE

11th TIGNES

12th SERRE CHEVALIER

13th MERIBEL (LES ALLUES)

14th VARS

15th LES HOUCHES

1. Introduction



4. Conclusion

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ELECTRE III allows to:

– Determinate a ranking who reflects the preference

– Bypass the problem of the full aggregation of incommensurate

performances

For a larger number of alternatives, the graph is highly complex

Perspectives :

– Actualize the data base

– Compare ELECTRE rankings with other rankings – for example

efficiency ranking (obtained with DEA method or others)

1. Introduction



4. Conclusion

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Thank you for attention!

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Compétitivité Destinations Neige MCDA CAEPEM ITEM

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