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strana 6 4 / 2011 E + M EKONOMIE A MANAGEMENT

IntroductionLand managers of different countries are often

faced with how to optimize road networks in orderto maintain all roads, trails and paths for the dif-ferent purposes and reducing negative impactson environment. Johnson et al. (2011) develo-ped a spatial MCDM (Multiple criteria decisionmaking) system to obtain weights of factors thathad significant impacts on both economic andenvironment, combine GIS (Geographic infor-mation system) data layers, and derive a prioritymap of all the roads for being closed. Stakehol-ders aiming to survive the competition battlehave to rethink the process of development and

reconstruction of supply chains, and the proce-ss which may feature peculiarities, depending onthe branch of industry. Jakimavicius and Burinski-en (2007) discussed problem o road networkdensity assessment in regions and proposedGIS decision support system based on two cal-culation methods: TOPSIS (Technique for OrderPreference by Similarity to Ideal Point) and SAW(Simple Additive Weighting). Strategy shapingof distribution network requires evaluation of

number criteria, which influence the distributionsystem (Vasiliauskas et al. 2010). To increasethe transportation effectiveness and quality, theinterested parties (groups of people) should tocoordinate their actions, cooperate with eachother in solving the problems and exchange re-levant information. Therefore, AHP (Analytic hie-rarchy process) methodology, based on pair wisecomparisons of criteria, is relevant technique todetermine criteria weights (significances) consi-

dering the data obtained from the respondentsand experts (Sivilevicius 2011). The problem ofselecting the most effective road investment pro-

jects is becoming more and more acute. Road in-vestment project alternatives have to be apprisedin an integer manner using mathematical modelsin addition to economic, social and environmentcriteria (Rudzianskaite-Kvaraciejieneet al.2010).

It should be noted that these aspects are inter-related and complementary, and therefore influ-ence the appraisal of road investment projects.Rudzianskaite-Kvaraciejiene et al.(2010) for this

purpose used expert and TOPSIS appraisal me-thods.

The Caspian Forest is located in the north ofIran. It covers the north facing slopes of the Al-borz Mountain ranges and is classified as a tem-perate mountain forest. The majority of this forestis managed as an uneven aged forest (Naghdietal. 2008). Forest roads are the most importantinfrastructural facilities to exploit forests that arerenewable natural resources. Vehicles release

large amount of heavy metals to environment (Mi-kalajn, Jakuionyt2011).Forest roads are generally planned and con-

structed by considering physical, economical,and environmental requirements (Naghdi, Mo-hammadi Limaei 2009). Tampre et al. (2009)presented modelling exercise aimed at ascertai-ning the effects of road pricing on a large roadnetwork. The main objective of this research isthe maximization of social welfare gain. A roadnetwork that leads us to our goals needs to beestablished in order to plan forestry activitiessustainably. In addition to forestry services, forestroads provide economic benefit for rural populati-on by enabling them to market their products andhelp them meet their healthcare, education andother social needs. Valente and Vettorazzi (2008)presented model based on ordered weightingaveraging method, integrated to a geographic in-formation systems, in the definition of priority are-as for forest conservation, also analytic hierarchy

process (AHP) is used. Forest roads interact withmany technical, economic, environmental and so-cial factors to render these services. It is highlyimportant to describe the capabilities of existingforest roads in terms o all functions assigned tothem in line with forestry objectives and to defi-ne their conditions of utilization in future (Gumus2009). At present, forests administrators are con-

FOREST ROADS LOCATING BASED ON AHP AND COPRAS-G

METHODS: AN EMPIRICAL STUDY BASED ON IRAN

Sarfaraz Hashemkhani Zolfani, Nahid Rezaeiniya, Edmundas Kazimieras

Zavadskas, Zenonas Turskis


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E + M EKONOMIE A MANAGEMENT 4 / 2011 strana 7

cerned with these issues and try to find suitableapproaches to reduce the costs and increasethe efficiency. On the other hand, not only cost

of road and wood transportation and extractionmust come down, but also environmental affecthave to come down too. In fact, the cost of woodtransportation and road constructing should bebalanced by a technical forest road network thataccesses the forest surface with the minimizedroad length (Naghdi, Babapour 2009). Manualroad planning in mountainous forests, conside-ring technical and environmental issues, is a di-fficult job. More recently, simultaneous informa-

tion management with respect to the importantfactors in road planning and rapid assessmentof the roads has been possible by using GIS ca-pabilities (Naghdi, Babapour 2009; Raafatnia etal. 2006). For this purpose, more recently resear-chers have been using different methods such aslinear programming method (Anderson, Nelson2009), fuzzy logic (Tiryana 2005), Tabu search(Aruga 2005), genetic algorithm Keshtiarast et al.2006) and using digital elevation model or DEM

to analyze all kinds of data on forest road locating.Naghdi and Babapour (2009) prepared the sta-bility map using soil texture and bed rock. Theirresults confirmed that using stability maps, GISand AHP can be a useful method for the planningof forest road networks in mountain areas in Iranand is preferred to previous and traditional me-thods. In Mazandaran province that locating in

the north of the Iran is so important to developedforest roads. The aim of this research is to selecta location for forest road in Haraz region. After

primary research three regions were selected forthis research that including: Kelerd, Pelet Chesh-me and Mangel. In this paper we use AHP me-thod for calculating the weights of each criterionthat shown in Table 3 and applied COPRAS-G(complex proportional assessment method withgrey interval numbers) method for selecting thebest place for construct the forest road.

1. MethodologyOver the past decades the complexity of econo-

mical decisions has increased rapidly, thus high-lighting the importance of developing and imple-menting sophisticated and efficient quantitativeanalysis techniques for supporting and aidingeconomical decision-making (Zavadskas, Turskis2011). Multiple criteria decision making (MCDM)is an advanced field of operations research, pro-vides decision makers and analysts a wide range

of methodologies, which are overviewed and wellsuited to the complexity of economical decisionproblems (Hwang, Yoon 1981; Zopounidis andDoumpos 2002; Figueira et. al. 2005). Multiplecriteria analysis (MCA) provides a framework forbreaking a problem into its constituent parts.MCA provides a means to investigate a numberof alternatives in light of conflicting priorities.

Fig. 1: Process of Locating

Source: own

Identification of selection criteria

Construction of

selection criteria

hierarchy

Final ranking results

Criteria weights by

AHP

Ranking with

COPRAS-G method

Literaturesurvey


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Over the last decade scientists and researchershave developed a set of new MCDM methods(Kaplinski, Tupenaite 2011; Kapliski, Tamosaiti-

ene 2010, Tamosaitiene et al. 2010, Kerulieneet al. 2010). They modified methods and appliedto solve practical and scientific problems.

Solving of modern decision making problemsin most cases is based on integrated model ofdifferent approaches. Zavadskas et al. (2008b)provided a review on international and nationalpractices in investment decision support toolsin bridges and road management. Cost benefitanalysis and multiple criteria analysis are principal

methodologies or this reason. It is stated that mul-tiple criteria analysis may be particularly helpfulin early stages of project development, strategicplanning. Cost benefit analysis is used most wide-ly for project prioritization and selecting the finalproject from the set of alternatives. Brauers et al.(2008) demonstrated that the concept of multi-ob-

jective optimization of road design alternatives itis suitable tool to determine the best road designalternative from feasible discrete alternatives set.

For instance, Choi et al. (2009) applied a newraster-based GIS model that combines multiplecriteria evaluation and least-cost path analysis todetermine the optimal haulage routes of dumptrucks in large scale open-pit mines. Vassou et al.(2006) stated that usually a subjective enginee-ring judgment process is adopted for selectingnew road alignment decisions, and so the needarose for a generic decision model to be deve-loped to support engineers and decision-makers

in selecting the optimal design. The AHP is usu-al tool for this reason. The association of SouthEast Asian Nations has recently decided to deve-lop a new highway network to connect countriesin the association by roads to enhance culturalintegration and economic growth of Asian coun-tries. Priotization of investments for 32 road se-ctions in the highway network construction wasevaluated using a two-step hierarchical fuzzymultiple criteria decision making process (Lee etal. 2011). Erden and Coskun (2010) presentedstudy which combines GIS and AHP to providedecision makers with a model to ensure optimalsite location(s).

1.1 Analytic Hierarchy ProcessAnalytic hierarchy process, proposed by Tho-

mas L. Saaty in 1971 (Saaty 1971, 1980), is able

to solve the multiple criteria decision making pro-blems. AHP utilize three principles to solve pro-blems (Aydogan 2011):

1) structure of the hierarchy,2) the matrix of pair wise comparison ratios,

and3) the method for calculating weights.

AHP can decompose any complex probleminto several sub-problems in terms of hierarchi-cal levels where each level represents a set ofcriteria or attributes relative to each sub-problem.During the past, there were 13 major conditionsthat have discovered to well fit the utilization of

AHP such as setting priorities, generating a set ofalternatives, choosing a best policy alternatives,determining requirements, allocating resources,predicting outcomes, measuring performance,designing system, ensuring system stability, op-timization, planning, resolving conflict, and riskassessment. Besides, recent conditions encom-pass to reduce the influence of global climatechange (Berrittella et al.2007), to choose univer-sity faculty (Grandzol 2005), to decide the loca-

tion of offshore manufacturing plants (Walailak,McCarthy 2002), to evaluate risk in conductingcross-country petroleum pipelines (Dey 2003),and to manage U.S. watersheds (De Steiguer etal.2003) and so on.

It has been well utilized in several fields thatrequire the chosen of alternatives and the weightexploration of evaluation criteria like business(Angelou, Economides 2009), industry (Chen,Wang 2010; Fouladgar et al. 2011), and heal-

thcare (Liberatore, Nydickl 2008). Coultier et al.2006 stated that the management of low-volumeroads has transitioned from focusing on mainte-nance designed to protect a capital investment inroad infrastructure to also include environmentaleffects. They used AHP method to solve this pro-blem. Callaos et al.(2003) applied model basedon AHP scale to select the route path. Forestand low-class roads substantially differ from mainroads in terms of extensiveness, databank availa-

bility, and profitability, which stress the need fora sophisticated decision making mechanism forassessment, selection and maintenance programs(Khademi, Sheikholeslami 2010). Khademi andSheikholeslami proposed for stakeholders to usepreliminary procedures including the Conferenceand the Delphi survey to determine the list of spe-cialists for the AHP and criteria which were thenfed into AHP. The problems solution results indi-


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cate the significant contrast between conventionalindividualistic decisions and those made throughincorporating systemic specialist comments.

Based on its unique merit, this method is usedin solving many sophisticated decision-makingissues by different researchers (Dong et al.2008,2010; Lin et al.2008; Wong, Li, 2008; Arunraj,Maiti, 2010; Plebankiewicz 2009; Lin 2010; Ulu-beyli, Kazaz 2009; Medineckienet al.2010).

The recent applications of AHP method inshortly are listed below:- Ananda and Herath (2008) AHP used to syn-

thesise stakeholder preferences related to

regional forest planning and to incorporatestakeholder preferences;

- Cebeci (2009) presented a fuzzy AHP appro-ach to select a suitable enterprise resourceplanning system for textile industry;

- Wu et al. (2009) applied fuzzy AHP (FAHP)and the three MCDM analytical tools of SAW,TOPSIS, and VIKOR were respectively adop-ted to rank the banking performance and im-prove the gaps with three banks;

- Podvezko (2009) considered an application ofAHP technique to more complicated cases;- Colombo et al. (2009) proved that judicious

use of AHP by experts can be used to repre-sent citizen views;

- Maskelinaite et al.(2009) solved problem ofquality of passenger carriage;

- Podvezko et al.(2010) ranked contracts;

- temberger et al.(2009) applied in businessprocesses management;

- Sivileviius and Maskeliunaite (2010) solved

problem of improving the quality for passengertransportation;

- Bojovic et al.(2010) applied it to determine ofan optimal rail freight car fleet composition;

- Steuten et al.(2010) used AHP weights to fillmissing gaps in Markov decision models;

- Hadi-Vencheh and Niazi-Motlagh (2011) pre-sented an improved voting AHP-data enve-lopment analysis methodology for suppliersselection;

- Yan et al.(2011) presented new developmentsand maintenances of the existing infrastructu-res under limited government budget and time.

The calculation of AHP is adopted ratio scalefor developing pair-wise comparison matrix. It typi-cally can be categorized into 5 sub-scales basedon different levels of importance: Equal importan-ce, somewhat more important, much more impor-tant, very much more important, and absolutelymore important. There are still 4 sub-scales with

each level of importance between above 5 majorsub-scales. Therefore, there is an amount of ninesub-scales. The ratio values from 1 to 9 are givento each sub-scale as we summarized in Table 1.

The calculation steps of AHP are presented asfollows (Saaty 1990):

Step 1. Establish the pair-wise comparison mat-rix A by using the ratio scale in Table 3-6:

Tab. 1: The Ratio Scale and Definition of AHP

Intensity ofimportance

Definition Description

1 Equal importance Two factors contribute equally to the objective.

3 Somewhat more importantExperience and judgment slightly favour one over theother.

5 Much more importantExperience and judgment strongly favour one over theother.

7 Very much more importantExperience and judgment very strongly favour one overthe other. Its importance is demonstrated in practice.

9 Absolutely more importantThe evidence favouring one over the other is of thehighest possible validity.

2, 4, 6, 8 Intermediate values When compromise is needed

Source: Saaty 1990


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- Uzsilaityte and Martinaitis (2010) investigatedand compared different alternatives for therenovation of buildings taking into account

energy, economic and environmental crite-ria while evaluating impact of renovation mea-sures during their life cycle;

- Chatterjee et al. (2011) presented materialsselection model based on COPRAS and EVA-MIX methods;

- Karbassi et al. (2011) applied COPRAS me-thod to solve effectiveness problem of energyusing in buildings;

- Podvezko (2011) presented comparative analy-

sis of MCDM methods (SAW and COPRAS).The procedure of applying the COPRAS-G me-

thod consists in the following steps (Zavadskaset al.2009):1. Selecting the set of the most important crite-

ria, describing the alternatives;2. Constructing the decision-making matrix X:

where

xij is determined byx

ij (the smallestvalue, the lower limit) and xij (the biggest va-

lue, the upper limit).3. Determining significances (weights) of the

criteria qj.

4. Normalizing the decision-making matrix X:

In formula (7) xijis the lower value of thej crite-rion in the alternative i of the solution; x

ij is the

upper value of the criterionjin the alternative iofthe solution; n is the number of criteria; mis thenumber of the alternatives compared.

Then, the decision-making matrix is normalized:

5. Calculating the weighted normalized decisi-on matrix . The weighted normalized va-lues x

ij^ are calculated as follows:

(9)

In formula (9), qj is the significance of the

jthcriterion. Then, the normalized decision-making matrix is:

6. Calculating the sums Pi of criterion values,

whose larger values are more preferable:

7. Calculating the sums Riof criterion values,

whose smaller values are more preferable:

In formula (12), (mk)is the number of crite-ria which must be minimized.

8. Determining the minimal value of Ri:

9. Calculating the relative significance of eachalternative Q

i:

10. Determining the optimality criterion K:

11. Determining the priority of the alternative.12. Calculating the utility degree of each alternative:

~

X =

[x11

; x11

] [x12

; x12

] ... [x1m

; x1m

][x

21

; x21

][x22

; x22

] ... [x2m

; x2m

]

[xn1

; xn1

][xn2

; xn2

] ... [xnm

; xnm

]

... ... .... . . ;~

~~

~~~~~

~~

~~~~

~~~~

(8)

X^

xij=x

ij q

j or x

ij= x

ij q

j and x

ij= x

ij q

j.^ ^ ^~ ~ ~

X =

[x11

][x12

] ... [x1m

][x

21][x

22] ... [x

2m]

[xn1

][xn2

] ... [xnm

]

......

.... . . =

[x11

; x11

] [x12

; x12

] ... [x1m

; x1m

][x

21; x

21][x

22; x

22] ... [x

2m; x

2m]

[xn1

; xn1

][xn2

; xn2

] ... [xnm

; xnm

]

.

..

.

..

.

..

. ..

.=

^

^

^

^^^

^

^

^

^^

^

^

^

^^^

^^

^^^^

^^^

^^

(10)

(xij x

ij).

12

k

j =1

^ ^Pi= (11)

(xij x

ij); j = k, m.

12

n

j =k+1

^ ^Ri= (12)

(13)Rmin= min Ri; j =j, m.i

Qi= P

i

Ri

Ri

1

Ri

m

mi = 1

i = 1

(14)

K = maxQi; i = 1, m.

i(15)

X =

[x11

][x12

] ... [x1m

][x

21][x

22] ... [x

2m]

[xn1

][xn2

] ... [xnm

]... ... .... . . =

[x11

; x11

] [x12

; x12

] ... [x1m

; x1m

][x

21; x

21][x

22; x

22] ... [x

2m; x

2m]

[xn1

; xn1

][xn2

; xn2

] ... [xnm

; xnm

]

......

.... . .

;= (6)

i= 1,mandj= 1,n,

xij=

~

=x

ij

( xij x

ij)12

m m

i = 1 i = 1

2xij

xij x

ij

m m

i = 1 i = 1

;

xij=

~

=x

ij

( xij xij)12m m

i = 1 i = 1

2xij

(xij xij)m

i = 1

; (7)

i= 1,mandj= 1,n.

Ni=

Qi

Qmax

100% (16),


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where Qiand Q

maxare the significance of al-

ternatives obtained from Eq. (14).

2. Forest Roads Locating Model Ba-

sed on AHP and COPRAS-G Method

2.1 Classification Criteria and Hie-

rarchical StructureIn this paper we classified important criteria for

forest road locating in Haraz region, Mazandaran,Iran. We consider criteria and identify themsel-

ves sub-criteria. The criteria and sub-criteria areshown in Table 3.

The criteria are considered three regions: Ke-lerd (A

1), Pelet cheshme (A

2) and Mangel (A

3).

Analytic Hierarchy Process is used to identifythe weights of criteria of forest road locating (Ta-ble 1). Its' hierarchical structure is shown in Fig.1.

This structure serves to prioritize criteria andsub-criteria, to choose best locations for forestroad.

2.2 Prioritization Criteria and Sub-

-Criteria for Forest Road LocatingFor pair wise comparison decision making,

a group of experts were invited to be the decisionmakers, because they are experienced. Informati-on about experts is shown in Table 4.

Paired comparison matrix of criteria is one ofthe matrices which were completed with informa-

tion of experts is shown in Table 4 as an example.AHP method is then used for prioritizing.

After all comparisons and weighing processesare done, the overall weight of each criterion andsub-criterion are obtained (Table 5). Accordingthe weights in Table 5, x

1, x

3-1and x

3-2were three

of the most important considering criteria.

Tab. 3: Criteria and Sub-Criteria for Forest Road Location

Criterion

Sub-

criterion References

x1

x1

Technical features (Gumus 2009), (Raafatnia 1988)

x2

Transportation

x2 - 1

Traffic volume (Gumus 2009)

x2 - 2

Quality & safety (Raafatnia 1988)

x3

Features

x3 - 1 Environmental features (Raafatnia 1988)

x3 - 2

Social features (Gumus 2009), (Raafatnia 1988)

x3 - 3

Economical features (Gumus 2009), (Raafatnia 1988)

x4

Usage

x4 - 1

Suitability for villagers (Raafatnia 1988)

x4 - 2

For agricultural activities (Raafatnia 1988)

x4 - 3 For security aimed usage (Gumus 2009), (Raafatnia 1988)x

4 - 4Progression to the mountainous areas (Gumus 2009)

x5

x5

Road construction with minimalexcavation

(Gumus 2009)

x6

x6

Destruction of wildlife habitats (Raafatnia 1988)

Source: own


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Prioritizing indicatorsand sub-indicators

for forest

road location

X6X5X4X3X2X1

X4-4X4-3X4-2X4-1X3-3X3-2X3-1X2-2X2-1

Fig. 2: The Hierarchical Structure for Prioritizing Indicators and Sub-Indicators for Forest Road

Locating

Source: own

Tab. 4: Background Information of Experts

Category Classification No.

Working backgroundAcademic field 8

Government unit 6

Education Level

Bachelor 1

Master 5

Ph.D. 8

SexMale 11

Female 3

Source: own

Tab. 5: Criteria Paired Comparison Matrix

CriteriaWeights

x1

x2

x3

x4

x5

x6

Criteria

x1

1 6 1/3 4 8 6 0.271

x2

1/6 1 1/6 1/3 4 4 0.084

x3 3 6 1 5 9 8 0.434x

41/4 3 1/5 1 6 5 0.138

x5

1/8 1/4 1/9 1/6 1 1/3 0.027

x6

1/6 1/4 1/8 1/5 3 1 0.045

C.I.=0.123 C.R.=C.I./R.I.= 0.10

Source: own


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Tab. 7 (continuation): Initial Decision Making Matrix with the Criteria Values Described in Intervals

x4-1

x4-2

x4-3

x4-4

x5

x6

Opt. max max max max max min

qj

0.048 0.016 0.066 0.008 0.027 0.045

Region x4 -1; x4 -1 x4 -2; x4 -2 x4 -3; x4 -3 x4 -4; x4 -4 x5; x5 x6; x6

A1

80 95 70 80 60 70 40 70 50 90 80 90

A2

90 95 70 85 70 80 40 70 40 60 70 80

A3

70 80 50 70 70 80 80 90 60 70 80 90

Source: own

Tab. 8: Normalized Weighted Decision Making Matrix

x4 -1 x4 -2 x4 -3 x4 -4 x5 x6

Opt. max max max max max min

Region x4 -1; x4 -1 x4 -2; x4 -2 x4 -3; x4 -3 x4 -4; x4 -4 x5; x5 x6; x6

A1

0.015 0.018 0.005 0.006 0.018 0.021 0.002 0.003 0.007 0.013 0.015 0.017

A2

0.017 0.018 0.005 0.006 0.021 0.025 0.002 0.003 0.006 0.009 0.013 0.015

A3

0.013 0.015 0.004 0.005 0.021 0.000 0.003 0.004 0.009 0.012 0.015 0.017

x1

x2 -1

x2 -2

x3 -1

x3 -2

x3 -3

Opt. max min max max max max

Region x1; x1 x2-1; x2-1 x2-2; x2-2 x3-1; x3 -1 x3-2; x3 -2 x3-3; x3 -3

A1

0.093 0.124 0.004 0.006 0.013 0.027 0.083 0.116 0.024 0.032 0.012 0.016

A2

0.062 0.108 0.004 0.007 0.020 0.023 0.066 0.116 0.032 0.036 0.010 0.012

A3

0.062 0.093 0.006 0.006 0.023 0.027 0.100 0.133 0.024 0.028 0.014 0.016

Tab. 8 (continuation): Normalized Weighted Decision Making Matrix

Source: own

Source: own

Tab. 9: Evaluation of Utility Degree

Region Pi

Ri

Qi

Ni

A1 0.324 0.021 0.345 100.00%

A2 0.299 0.019 0.321 93.11%

A3 0.303 0.022 0.322 93.49%

Source: own


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Sarfaraz Hashemkhani Zolfani

Shomal UniversityDepartment of Industrial Engineering

[email protected]

Nahid Rezaeiniya

Alghadir Institute of higher educationDepartment of Industrial Engineering

Edmundas Kazimieras Zavadskas

Vilnius Gediminas Technical UniversityFaculty of Civil Engineering

[email protected]

Zenonas Turskis

Vilnius Gediminas Technical UniversityFaculty of Civil Engineering

[email protected]

Dorueno redakci: 4. 6. 2011Recenzovno: 8. 8. 2011, 10. 8. 2011Schvleno k publikovn: 20. 9. 2011


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ABSTRACT

FOREST ROADS LOCATING BASED ON AHP AND COPRAS-G METHODS: AN EMPIRI-

CAL STUDY BASED ON IRAN

Sarfaraz Hashemkhani Zolfani, Nahid Rezaeiniya, Edmundas Kazimieras

Zavadskas, Zenonas Turskis

Forest roads have an important role in the forest management and economic of countries. TheCaspian forest is the most important forests region in Iran. Its so important to construction offorest roads in the best place that can be useful. Haraz region is located in Mazandaran provincethat is near to Alborz Mountain. Selection the best place for construction of a road in forest is an

important problem. For this research three place are considered for evaluating in Haraz regionthat including: Kelerd, Pelet Cheshme and Mangel. In this paper we applied hybrid MCDM me-thods for evaluating the regions. AHP applied for calculating the weight of each criterion and sub

criterion and then COPRAS-G method applied for evaluating the places for selecting the bestplace for constructing the forest road. Result showed that Kelerd region in the best place for thiswork.

Key Words:forests road, Caspian forest, AHP, COPRAS-G method.

JEL Classification: C02, C44, D81, L74.

mcdm-copras g

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