debmallya chatterjee: delphi-fahp and promethee: an

TWP: 111_1505

Delphi-FAHP and PROMETHEE:

An integrated approach in healthcare facility location selection

by

Debmallya Chatterjee Associate Professor,

T.A Pai Management Institute,

Manipal-576104, Karnataka, India.

Phone: 0820- 2701023, email: [email protected]

Working Paper No: TWP No. 111 / 2015-16 / 05

Abstract: Fuzzy AHP and PROMETHEE-GAIA are among the two most prominent MCDM

tools used by the researchers in facility location selection. However both the methods have

their weaknesses reported by the researchers. AHP suffers the problem of size and developing

consensus where PROMETHEE can be improved with the hierarchical structure of AHP. This

paper makes an attempt to embed Delphi to Fuzzy AHP and then integrate it with

PROMETHEE-GAIA to develop a robust model in healthcare facility location selection. The

purpose is to overcome the weaknesses of both the individual methods by this amalgamation.

A case of thirteen alternative locations from six different subdivisions from India is considered

to demonstrate the applicability of this integrated method.

Key words: Delphi, Fuzzy AHP, PROMETHEE, GAIA, healthcare, location selection

Debmallya Chatterjee: Delphi-FAHP and Promethee: An integrated Approach to Healthcare Facility..… TWP111_1505

1

Delphi-FAHP and PROMETHEE: An integrated approach in healthcare facility location selection

1 Introduction

Facility location selection has always been one of the major multi criteria decision making

problems that researchers talked about in last couple of decades. Because of its multicriteria

nature this decision making attracted different tools including analytical hierarchy process

(AHP) developed by Saaty (1980). A number of researchers used AHP or its fuzzy extension

in facility location selection where they provided evidence on the efficacy of this MCDM tool

(Shim, 1989; Jesuk, 2005; Cheng-Ru, 2007). Similarly the outranking method known as

Preference Ranking Organization MeTHod for Enrichment Evaluations (PROMETHEE)

developed by Brans and Vincke (1985), was also extensively used by researchers in various

field of decision making including facility location selection (Behzadian et al., 2009; Walther

et al., 2008; Frikha et al., 2011; Athawale et al., 2012).

However in spite of an extensive use, researchers identified some perennial problems with AHP

and its fuzzy extensions. First the process of generating consensus among the expert opinions

more or less relies on an aggregation method with a high chance of losing important

information (Macharis et al., 2004). In fact researchers are of the opinion that generating

consensus can improve accuracy in results and thus can be beneficial to the outcomes (Okoli

and Pawlowski, 2004, Soltani et al., 2011). Second, most of the studies demonstrate the

applicability of this MCDM tool only with a limited number of alternatives. With a set of 10

alternatives the numbers of pair wise comparisons become 45 and with 25 alternatives it

reaches 300.Third is to limit the scale to a 9 point scale which cannot cope with the fact that an

alternative can be multiple times better that the other (Macharis et al., 2004). Interestingly the

first problem can be overcome by embedding Delphi technique with AHP. The second and the

third problems can be addressed using the PROMETHEE method. In fact there can be any

number of alternatives that can be compared using this outranking method without much

difficulty and also it has no restriction on scale. However few studies also voiced for improving

PROMETHEE by embedding AHP or its fuzzy extension (FAHP). The ability of AHP to

decompose the decision problem and building hierarchy of criteria can improve the generation

of criteria weights in PROMETHEE. Studies including Giannopoulos and Founti (2010),

Venkatesan and Kumanan (2012) and also Bansal and Kumar (2013) demonstrated an efficacy

in the applications of AHP-PROMETHEE hybrid approach in different fields of study.


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Among all other facility location selection problems health care facility location selection using

MCDM tools got importance since last decade. Not only physical access and other criteria

affects the quality and operations of the medical services provided through the facility but also

the success or the failure of such a facility depends largely on the location selected (Paul, 1997;

Kuo et al., 1999). In last couple of decades a good number of researchers acknowledged the

need to analyze this location selection decisions in health care sector and applied different

MCDM tools to facilitate the decision making (Hanes and McKnight, 1984; Paul and Batta,

2008). A large amount of such decision making was done with the application of AHP or its

fuzzy extension (FAHP). Researchers like Wu et al (2007) used AHP to select location for the

Taiwanese hospitals in obtaining competitive advantage. Vahidnia et al (2009) used FAHP and

GIS to select location for hospitals in Tehran metropolitan area. In a similar approach Soltani

and Marandi (2011) tried to address the hospital location selection in Shiraj metropolitan area,

Iran by combining AHP and GIS.

Nevertheless the present study did not come across any prior work in health care facility

location selection where the researchers used Delphi to generate consensus among expert

opinions in an AHP or FAHP environment. The use of PROMETHEE in the domain is also not

available. Though some researchers like Bansal and Kumar (2013) used AHP-PROMETHEE

hybrid method in decision making but such a study is absent in health care facility location

selection.

The aim of this paper is to study the selection of health care facility location using an integration

of Delphi embedded FAHP and PROMETHEE approach. Six sub criteria under three main

criteria are selected from literature to evaluate the alternative locations. To clearly understand

importance and the conflicts between criteria, geometric analysis of interactive aid (GAIA) is

also used. A case of thirteen alternative locations from six different sub divisions of the district

of Burdwan, West Bengal, India is considered for illustration.

The paper is organized as follows: Detailed overview of the techniques such as FAHP, Delphi,

PROMETHEE-GAIA with their applicability are given in section 2. In section 3 the

methodology of the research is presented. Section 4 demonstrates the application of the

integrated approach of Delphi-FAHP and PROMETHEE in health facility location selection in

India. Section 5 analyses the results and finally conclusion, limitation and suggestions for

future studies are given in section 6.


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2 An overview of FAHP, Delphi and PROMETHEE-GAIA approaches

2.1 Fuzzy AHP (FAHP)

Succeeding the work of Saaty (1980) several researchers used AHP in their studies that can be

illustrated using the following steps:

First the objective is identified and the overall hierarchy of the decision problem is designed.

This hierarchy reveals the various factors to be considered as well as the various alternatives

in the decision. There may be multiple levels within the hierarchy based on the sub factors

available under each factor.

Both qualitative and quantitative factors can be compared using pair wise comparisons and

processed through a comparison matrix, which generates factor weights. Factor weights are

numerical values quantifying the importance of the factor in the decision. The column average

of all these factor weights is used to check the consistency and is denoted by λaverage.

Check the consistency ratios of the pair wise comparison matrices to ensure consistency in

judgments. A response matrix failing to satisfy the consistency test is rejected. The consistency

index (CI) for each matrix of order n is computed by the formulae:

CI = (λaverage-n)/(n-1). The consistency ratio (CR) is then calculated using the formulae: CR =

CI/RI, where RI is a known Random Index. This consistency test validates the responses taken

into the model. A response is valid if the value of CR is found to be less than 0.1.

The alternatives are compared with respect to the factors or sub factors in the hierarchy and

the scores are aggregated to obtain overall rating of an alternative. Here each of the sub factor

weights are multiplied to the corresponding factor weights to generate overall weights of the

sub factors denoted by Sl. The overall score of mth alternative is obtained by Am in equation

(1.1)

1

........................(1.1)N

m l ml

l

A s a

where lsis the weight of lth sub factor and mla

is the weight of thm alternative with

respect to lth sub factor.

Since T. L. Saaty introduced AHP in 1980’s a number of researchers used this MCDM tool in

complex decision making situations with a good amount of success (Zahedi, 1986; Altay,

2008). However researchers also felt that this tool is not capable of handling subjectivity and


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vagueness inherent in human judgments. Zimmermann (1990) and then Chen and Mon (1994)

recommended the inclusion of fuzzy set theory (Zadeh, 1965) in AHP to address this issue.

Fuzzy AHP (FAHP) differs from AHP in terms of the linguistic scale and the computations

done in generating weights and importance. The linguistic scale consisting of five triangular

fuzzy numbers (TFN), proposed by Chen (2000) and further modified by Pan (2008) is used to

capture the expert responses. Normalization of geometric mean (NGM) method is used to

generate the fuzzy weights and further defuzzification is done using signed distance method

(Yao and Wu, 2000) to obtain non-fuzzy or crisp importance.

2.2 Delphi Technique

Delphi technique has evolved as an efficient and effective tool in qualitative decision making

since last couple of decades. The technique is widely used mainly in structuring processes and

generating consensus among responses (Okoli and Pawlowski, 2004; Azizollah, 2008). This

technique asks for reliable responses on problems or conflicting situations from the experts in

a panel. This tool helps the researchers in combining the reports or feedbacks of a group of

experts into one consolidated statement (Wanda and Tena, 2004). The process starts with

accumulating the expert responses and preparing a consolidated report. Though there is no

restriction on the number of experts in such a panel but Okoli and Pawlowski (2004)

recommended that 10-18 experts in such a panel may be adequate. In the next round the

consolidated report is sent to the experts along with their respective responses of the previous

round for modification or alteration, if any. After successive rounds the responses are expected

to converge. To avoid any sort of influence the anonymity of the experts are maintained

throughout the process.

2.3 PROMETHEE and GAIA

PROMETHEE is an interactive approach widely used in the context of multicriteria decision

making (MCDM) in different fields of study (Brans and Vincke, 1985; Hokkanen and

Saleminen, 1997; Athawale and Chakraborthy, 2010; Frikha et al., 2010; Rao and Patel, 2010;

Athawale et al., 2012; Jihen et al., 2012;). The backbone of PROMETHEE is the multicriteria

table containing the criteria, their importance or weights, details of the actions or alternatives

with respect to the criteria selected and the preference function. A preference function (Pj)

transforms the difference between scores obtained by two alternatives or actions with respect

to a particular criterion into a number between 0 and 1. Six different functions are proposed


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by Brans and Vincke (1985) which can be used in different criteria as deemed applicable

(Hokkanen and Salminen, 1997).

Another important aspect is the value function ϕ(a) corresponding to an alternative ‘a’. This is

also termed as net flow of alternative ‘a’. It is computed as the difference of ϕ+(a) and ϕ-(a)

where ϕ+(a) and ϕ-(a) represents the strength and weakness of vis-à-vis the other alternatives.

Where PROMETHEE I provides a partial ranking of the alternatives, a complete ranking with

respect to the net flow ϕ(a) can be obtained through PROMETHEE II.

GAIA is a multi-dimensional graphical representation which applies a principal component

analysis on the multi-dimensional space with respect to individual criterion net flow. It is a

visual descriptive analysis tool that helps in better understanding. Nemery et al. (2011) talked

about the usefulness of this method in MCDM.

3 Methodology

The methodology presented in this paper is divided into five sub sections. In the first section

Selection of experts, Identification of criteria and sub criteria, Shortlisting of alternative

locations are done. In the second section expert opinions are collected through questionnaire

framed using the fuzzy AHP scale. Further Delphi method is used to develop consensus among

expert opinions. In the third section criteria and sub criteria weights are generated using FAHP.

Data corresponding to all the alternative locations are fitted into PROMETHEE to generate net

flow of the alternatives and finally using the net flow the alternative locations are ranked.

4 Application

4.1 Selection of Experts

FAHP and PROMETHEE both being subjective decision making methods require consistent

inputs. In case of FAHP inconsistent responses may fail consistency test and the entire data

may be rejected (Saaty, 1980). This situation advocates for ‘expert opinion’ in criteria weight

generation through FAHP. In the present study 20 experts are selected for capturing responses.

The experts are medical doctors having more than fifteen years of experience in the field of

hospital or health care administration & projects and are quite acquainted of the heath

conditions of the district of Burdwan, West Bengal, India. Five of them are senior medical

practitioners having an average work experience of 36 years where as the other fifteen have in

between 20 to 35 years. A questionnaire is provided to the experts and are requested to do pair


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wise comparisons among criteria (or sub criteria) using the fuzzy linguistic scale (Pan, 2008).

Another questionnaire is also provided to capture information regarding the alternatives with

respect to four criteria using 9 point likert type scale ranging between very bad to very good.

4.2 Identification of criteria and sub criteria

Location selection criteria for health care facility are found to vary across different research

works in different country however in almost every piece of work researchers recommended

multi criteria evaluation of potential locations. Some of them talked about distance from arterial

routes, travel time, environmental issues, land cost and population density as the set of

important criteria (Vahidnia et al., 2009) where others opinioned for access to roads network,

distance to other medical centers, population density, environment and size of the land (Soltani

et al., 2011). Wu et al. (2007) considered population size, age, density, governmental policies,

land, labour and capital where Schuurman et al. (2006) considered socio demographics of the

service area, proximity to future expansion, space and population density while defining the

hospital catchment through travel time. Based on the prominence and repetitions of the criteria

in the available literature and on considering the valuable judgment of experts, this study

considers three major criteria and six sub criteria in the evaluation. The criteria and sub criteria

are summarized in Table 1.

Table 1: Criteria and sub criteria

Major Criteria Sub Criteria

Cost (CST) Cost of land (COL)

(Soltani et al., 2011; Vahidnia et al, 2009; Wu et al, 2007) Cost of construction (COC)

Population characteristics (PC) Population density (PD)

(Soltani et al., 2011; Schuurman et al, 2006;

Vahidnia et al, 2009; Wu et al, 2007) Economic condition (EC)

Location (L) Access to road network (ARN)

(Vahidnia et al, 2009; Soltani et al., 2011; Estill, 2006) Environment (ENV)


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4.3 Identification of the alternative locations for the study

The region of study consists of thirteen blocks within five sub divisions or clusters in the district

of Burdwan, West Bengal, India. An area covering more than 2755 square kilometers and a

total of more than two millions of populations without a hospital. From the medical facility and

population of these sub divisions of the district of Burdwan, West Bengal one can witness a

significant lacuna in terms of the availability of health care facilities. Table 2 provides us the

picture clearly. The number of hospital beds per 10000 populations is even poorer than the

nation’s average of 9. From Figure 1 the locations of these thirteen blocks can be identified.

Table 2: Health and population detail of the alternative locations

Cluster/

Sub

division

Blocks Area in

KM2

Population Density

(Persons per

KM2)

No. of hospital

Doctors

Male Female Total

C1 Ausgram-I 164.5 54623 52190 106813 649 0 8

Galsi-II 277.9 68641 65310 133951 482 0 6

C2

Jamalpur 267.88 123728 119746 243474 909 0 8

Raina-I 266.44 83633 79288 162921 611 0 4

Khandaghosh 256.13 87671 82639 170310 665 0 6

C3

Faridpur

Durgapur 144.6 59253 49366 108619 751 0 4

Pandabeswar 97.89 79992 66453 146445 1496 0 3

Kanksa 270.78 78669 72586 151255 559 0 7

C4

Purbasthali-II 188.18 97024 91125 188149 1000 0 7

Kalna-I 161.34 97903 92784 190687 1182 0 6

Monteshwar 305.4 109544 103718 213262 698 0 6

C5 Ketugram-I 189.86 74513 71500 146013 769 0 5

Katwa-II 164.45 61696 58618 120314 732 0 5

Total: 2755.35 1076890 1005323 2082213 0 75

Source: medical facility and population, bardhman.gov.in


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Figure 1: Map of the district of Burdwan, West Bengal, India

Source: NREGS, Bardhaman district1

4.4 Collecting expert responses and developing consensus using Delphi

The responses were collected to generate criteria weights and also to assess the alternative

locations with respect to some criteria for which data is not available. Six questions are

administered to compare the criteria and sub criteria using the fuzzy pair wise comparison scale

proposed by Pan (2008). Another four questions were administered to assess all the thirteen

alternatives with respect to four sub criteria using a 9 point likert type scale. After the data has

been collected a summary sheet is prepared and sent to the experts to have a relook at their

responses for possible alteration. Table 3, 4 and 5 respectively demonstrate the successive

rounds of Delphi and the associated convergence. The convergence process was terminated

after achieving eighty or more percentage of convergence to a point in the scale.

Table 3: Responses after first round of Delphi

Main criteria comparisons

Extremely un imp

Un imp

Equally imp Imp

Extremely imp

CST 15 5 PC

CST 7 12 1 L

PC 9 11 L

sub criteria comparison under cost

COL 11 9 COC

sub criteria comparison under population characteristics

PD 3 8 9 EC

sub criteria comparison under location

ARN 3 12 5 ENV

1 NREGS, Burdwan district (http://nregsburdwan.com)


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Table 4: Responses after second round of Delphi


Extremely un imp

Un imp Equally imp

Imp Extremely imp

CST 17 3 PC

CST 5 15 1 L

PC 4 16 L


COL 6 14 COC


PD 1 5 14 EC


ARN 3 15 2 ENV

Table 5: Responses after third round of Delphi


Extremely un

imp Un imp Equally imp Imp Extremely imp

CST 19 1 PC

CST 3 16 1 L

PC 2 18 L


COL 3 17 COC


PD 1 1 18 EC


ARN 1 17 2 ENV

4.5 Generation of criteria weights and scores for the alternatives

After the process of convergence by Delphi technique, the data is processed using the FAHP

to generate criteria weights. Each of the pair wise comparison table is tested for consistency

before considering them as inputs. These criteria weights are used to evaluate the alternatives

through PROMETHEE multicriteria table. Table 6 provides the detail of the multicriteria table

used to evaluate the alternatives by visual PROMETHEE software, version 1.4. GAIA is

further used to visually comprehend the conflicts among the alternatives with respect to each

criterion. The quantitative detail of the alternatives corresponding to population density and

economic condition are captured from Table 2.


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Table 6: Multicriteria table of PROMETHEE

Cost of

land

Cost of

construction

Population

density

Economic

condition

Access

to road

network Environment

Unit/ Scale 9-point 9-point

no. of people

/ sq. km.

livelihood

index(1000) 9-point 9-point

Preferences

Min/Max Min min Max Min max max

Weight 0.26 0.14 0.32 0.08 0.13 0.07

Preference Function Linear Linear Linear Linear Linear Linear

Thresholds absolute absolute Absolute Absolute absolute absolute

Statistics

Minimum 3 4 482 444 1 3

Maximum 7 7 1496 682 9 7

Average 5 5 808 506 6 5

Standard Dev. 1 1 268 61 2 1

5 Results and Discussions

The pair wise comparison using FAHP results in the determination of weights (both fuzzy and

crisp) for the criteria and sub criteria. See Table 7. Among the criteria, cost and population

characteristics have evolved as the most important criteria sharing equivalent importance.

Table 7: Criteria weights

Criteria Weight of main criteria Defuzzified

weight

(l m u) w

Cost (CST) 0.420 0.392 0.380 0.396

Population Characteristics (PC) 0.420 0.392 0.380 0.396

Location (L) 0.160 0.216 0.241 0.208

From the weights of the sub criteria in Table 8 one can witness that population density emerges

as the most important sub criteria with 31.6 % weight followed by cost of land with 25.5%.

Table 8: Sub criteria weights

Sub criteria L M U Weight within criteria Global weight

COL 0.710 0.634 0.600 0.645 0.2552

COC 0.290 0.366 0.400 0.355 0.1407

PD 1.000 0.750 0.691 0.798 0.3158

EC 0.000 0.250 0.309 0.202 0.0800

ARN 0.710 0.634 0.600 0.645 0.1341

ENV 0.290 0.366 0.400 0.355 0.0740


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The visual PROMETHEE software provides the partial ranking and the complete ranking of

the alternatives using PROMETHEE I and PROMETYHEE II. The indifference and preference

thresholds for the criteria can be observed from Table 9.

Table 9: Indifference and preference thresholds of the criteria

Sub Criteria Indifference threshold (q) Preference threshold (p)

Cost of land 1 2

Cost of construction 1 2

Population density 254 556

Economic condition 63 126

Access to road network 1 2

Environment 1 2

Table 10 demonstrates the rankings of the alternatives with respect to net dominance flow ϕ(a).

Table 10: Ranking of alternatives with respect to net flow

Rank Alternatives ϕ ϕ + ϕ -

1 Kalna-I 0.4452 0.4572 0.0119

2 Pandabeswar 0.3565 0.5412 0.1847

3 Kanksa 0.1700 0.2919 0.1219

4 Faridpur Durgapur 0.1446 0.2564 0.1118

5 Katwa-II 0.1393 0.1900 0.0508

6 Galsi-I 0.1023 0.2033 0.1011

7 Jamalpur -0.0824 0.0978 0.1802

8 Purbasthali-II -0.1267 0.1126 0.2393

9 Ausgram-I -0.1318 0.0800 0.2118

10 Khandaghosh -0.1751 0.0518 0.2269

11 Monteshwar -0.1818 0.0523 0.2341

12 Ketugram-I -0.277 0.0452 0.3222

13 Raina-I -0.3832 0.0524 0.4356

From the GAIA plane in Figure 2 one can visualize the conflicts among criteria very clearly.

Here the quality of information provided by the two dimensional GAIA plane is 89.1%.


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Figure 2: GAIA Plane

From figure 2 it is easy to identify that the alternative Pandabeswar (A7) is very strong in terms

of population density whereas Galsi-I (A2), Katwa II (A13) and to some extent Kanksa (A8)

are strong competitors in terms of access to road network. In the cost dimension Kalna I (A10)

and Faridpur-Durgapur (A6) are much stronger than the other competitors. A group of locations

including Monteshwar (A11), Jamalpur (A3), Khandaghosh (A5), Purbasthali II (A9),

Aushgram I (A1) and Ketugram (A11) are equivalently positioned with respect to environment,

however no location can be found strong enough with respect to economic conditions.

The sensitivity analysis shows that the alternative Pandabeswar, though in second position,

cannot be raised to the first position by changing the weights of the sub criteria cost of land,


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economic conditions, access to road network and environment. However by changing the

weight of cost of construction from 14% to 36% or population density from 36% to 46% may

revise the ranks of Kalna I and Pandabeswar. On critically analyzing the FAHP hierarchy one

can realize that raising either the weight of cost of construction to 36% or population density

to 46% are not feasible as they both asks for assigning more than 100% weightage on their

respective parent criteria.

6 Conclusion

This problem demonstrates an application of Delphi embedded FAHP and PROMETHEE -

GAIA approach in generating criteria weights and in evaluating potential health facility

locations in the district of Burdwan, West Bengal, India. The idea was to study how the strong

characters of Delphi embedded FAHP and PROMETHEE GAIA can be integrated successfully

to evaluate potential health facility locations. This integration helps us develop consensus

among expert opinions leading to better accuracy in results (Soltani et al., 2011). It also helps

us understand the similarities and conflicts among different criteria in the evaluation of

potential locations. The GAIA plane visually portrays the similarity of strengths of different

alternative locations with respect to different criteria. Sensitivity analysis performed on the

weights of the sub criteria through PROMETHEE shows that even after a reasonable alteration

of sub criteria weights, there is hardly any change in the first two positions of the alternative

rank list. This analysis certifies robustness of this integrated MCDM model.

This model can be used in similar real world applications in any field of study. In fact extension

of this work can be done by segmenting the locations with respect to sub divisions and

capturing the constraints existing within those sub divisions. Due to non-availability of reported

data within sub divisions and paucity of time in collecting data directly from field, the present

work was limited to the selection of a location representing a sub division. The work can be

more insightful if study can be done in selecting potential locations within sub divisions.

Though embedding Delphi resulted in better accuracy as reported by researchers, there is a

small glitch as well. The process of developing consensus among experts in Delphi may take a

lot of iterations to converge. In worst case it may also lead to divergence. The personal traits

of the experts may also shape the process of convergence.


14

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About Author

Dr. Debmallya Chatterjee

Associate Professor

Area: Operations Management

Education: M.Tech (NIT-D), MSc(Univ.of Burdwan), MIT(MAHE), PhD Telephone: +91-820-2701023

Email: [email protected]

Teaching: Operations Research, Application of Quantitative methods in Management, Business Statistics,

Discrete Mathematics, Business Mathematics, Mathematical Analysis, Problem solving and decision making.

Professional Activities: He obtained his Masters of Technology (Mtech) on ‘Operations Research in

Industry and Business management’ from NIT Durgapur. Prior to that he got his masters in Mathematics from

University of Burdwan and Masters of Information Technology from Manipal academy of higher education. He

is in academia for last eight years.

Research: Research interest is in the field of Fuzzy mathematics, Analytical hierarchy process and its

application in business, Systems thinking tools in creative decision making.

Publications:

1. Sinha, T.R., Chatterjee, D.and Iskanius, P. (2011) ‘Measuring stress among hospital nurses: an empirical

study using fuzzy evaluation’, Int. J. Logistics Economics and Globalisation, Vol. 3, Nos. 2/3, pp.142–

154.

2. Ghosh, A.,Chatterjee, D. and Ghosh, B.(2011), ‘Conceptual framework of faculty performance

evaluation’ Asian Journal of Management Research, Spl Issue 1, pp. 217-229

3. Chatterjee, D.(2010) ‘Fuzzy cognitive map of e-commerce customer satisfaction’, ICCS2010,

University of Burdwan

4. Chatterjee, D.(2010) ‘A Fuzzy Evaluation of service quality gap: A case study’, International Journal of

Advances in science and Technology, Volume. 1, Spl Issue 4, pp. 6-14

5. Chatterjee, D., Chowdhury, S. and Mukherjee, B., (2010), ‘Study of fuzzy-AHP model to search the

criterion in the evaluation of the best technical institutions: a case study’, International Journal of

Engineering Science and Technology, Vol. 2, No. 7, pp. 2499-2510

6. Chatterjee, D., Chowdhury, S. and Mukherjee, B., (2010), ‘A study of the application of fuzzy analytical

hierarchical process (FAHP) in the ranking of Indian banks’, International Journal of Engineering

Science and Technology, Vol. 2, No. 7, pp. 2511-2520

7. Chatterjee, D. (2008), ‘Understanding Cellular Automata: An Approach’, MID Journal of Computer

Application & Business Administration, Vol. 1, No. 1, pp 28-34.

Conference Presentations:

1. Presented a paper entitled “A Fuzzy-AHP evaluation of performance Appraisal: A case study on Peerless

general finance corps. ltd, kolkata” at the UGC sponsored National seminar at BB College, Asansol, W.B

on 19th – 20th Feb, 2010

2. Presented a paper entitled” Measuring service quality gap: A case study on City Residency Hotel,

Durgapur” at The Department of Business Administration, North Bengal University on 29th -30th

March, 2010

Awards & Recognitions

1. “Outstanding service 2010” Rahul Foundation, Rajbandh, Durgapur-12, West Bengal

2. “Best Faculty 2004-2005” Rahul Foundation, Rajbandh, Durgapur-12, West Bengal

mailto:[email protected]

debmallya chatterjee: delphi-fahp and promethee: an

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