Vinodh 2014

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Accepted Manuscript

Integrated fuzzy AHP- TOPSIS for selecting the best plastic recycling method:

A Case Study

S. Vinodh, M. Prasanna, N. Hari Prakash

PII: S0307-904X(14)00110-3

DOI: http://dx.doi.org/10.1016/j.apm.2014.03.007

Reference: APM 9895

To appear in: Appl. Math. Modelling

Received Date: 31 January 2012

Revised Date: 11 February 2014Accepted Date: 7 March 2014

Please cite this article as: S. Vinodh, M. Prasanna, N. Hari Prakash, Integrated fuzzy AHP- TOPSIS for selecting

the best plastic recycling method: A Case Study,Appl. Math. Modelling(2014), doi:http://dx.doi.org/10.1016/j.apm.

2014.03.007

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Integrated fuzzy AHP- TOPSIS for selecting the best plastic recycling

method: A Case Study

S.Vinodh1,*, Prasanna, M.2and Hari Prakash, N.3

* Corresponding author

1Assistant Professor, Department of Production Engineering, National Institute of Technology,

Tiruchirappalli-620 015, Tamil Nadu, India

Email: [email protected]

2,3Graduate Student, Department of Production Engineering, National Institute of Technology,

Tiruchirappalli-620 015, Tamil Nadu, India

2Email: [email protected]

3Email: [email protected]

Abstract

Due to the rapid depletion of natural resources and undesired environmental changes in a global

scale, it is necessary to conserve the natural resources and protect the environment. Industries

which manufacture plastic based products have the necessity to recycle plastics. There are

number of methods to recycle plastics. Since the selection of the best recycling method involves

complex decision variables, it is considered to be a multiple criteria decision-making (MCDM)

problem. This article develops an evaluation model based on the fuzzy analytic hierarchy process

(AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) to

enable the industry practitioners to perform performance evaluation in a fuzzy environment. The

purpose of the study is to determine the best method for recycling plastics among the various

plastic recycling processes. By observing the results, it is identified that mechanical recycling

process is found to be the best plastic recycling process using the integrated approach.

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Keywords:Multi-Criterion Decision Making; Analytic Hierarchy Process; TOPSIS; Recycling;

Plastics

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CCi: Closeness coefficient of ith plastic recycling method

1. Introduction

Due to the rapid depletion of natural resources, many organizations have realized that recycling

of used products is important to achieve competitive advantage (Kaya and Kahraman 2011).

Plastics recycling have become one of the most important processes in manufacturing

organizations which produce plastic products. There are number of processes in recycling

plastics (Wienaah 2007). Selecting the best process for recycling plastics involves complex

decisions. Such a problem can be solved using Multi Criteria Decision Making (MCDM)

approach. MCDM approach has become a main area of research for dealing with complex

decision problems. There are many studies that investigated the method about performance

evaluation among the given alternatives. In the literature, there are few fuzzy based methods

aimed at evaluating the relative performance considering multiple dimensions (Dagdeviren et al.

2009). The main purpose of this study is to utilize Analytic Hierarchy Process (AHP) and

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method under fuzzy

environment to identify and rank the best alternatives among the various plastic recycling

processes. Integrated Fuzzy AHP and TOPSIS have already been applied to evaluate the

performance of global top four notebook computer Original Design Manufacturing (ODM)

companies (Sun 2010). This combination has not been yet explored in automotive component

manufacturing industries to identify the best alternative among the various plastic recycling

methods. In this case study, Fuzzy AHP is used to determine the preference weights of

evaluation. Then, the weights are adopted in fuzzy TOPSIS to improve the gaps of alternatives

between real performance values and achieving aspired levels in each dimension/criterion and

find out the best alternative for achieving the aspired/desired levels based on three recycling

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processes. The three recycling processes include chemical recycling, mechanical recycling and

energy recovery process (Wienaah 2007). The scope of the research study is to identify the best

recycling process among the alternatives for an automotive component manufacturing industry.

The identified best recycling method is subjected to implementation in the case organization.

2.Literature Review

The literature has been reviewed from the perspectives of plastics recycling and integrated

MCDM methods

2.1 Literature review on plastics recycling

Fletcher and Mckay (1996) proposed a model for recycling plastics and discussed about the

reduction of waste due to various recycling methods. They discussed about the total waste

obtained from the plastic recycling process and its importance. Richard et al. (2011) proposed a

device configuration for the optimization of recovery of plastics for recycling using density

media separation cyclones. They suggested it to be economically viable for industrial plastics

recycling operations and producing a number of different plastics with purity to be used as a

substitute for virgin material. Patel et al. (2000) have assessed the recycling and recovery

processes for plastics waste from all sectors in Germany in terms of their potential contribution

due to energy saving and Carbon di oxide abatement. They showed that plastics waste

management offers scope for reducing environmental burdens. Shent et al. (1999) summarized

the importance of plastic waste recycling and plastic waste separation based on review of plastics

waste recycling and the floatation of plastics. They concluded that the flotation of plastics is a

fairly flexible technique and could prove to be a useful process for the separation of mixtures of

several different types of plastics. Subramanian (2000) suggested the increasing awareness of

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plastics recycling and waste management in US. This study considered life cycle analyses and

management in plastics recycling as tools for decision making. Pacheco et al. (2012) have

studied about an overview of plastic recycling in Rio de Janeiro. The objective of this study was

to show how the plastic recycling has been carried out to indicate its own difficulty besides the

evaluation for recycling of post consumed plastics. Curlee (1986) discussed about the economic

and institutional issues in plastics recycling. The two major objectives of this study was to

discuss the quantities of plastic wastes that are candidates for different types of recycling process

and to discuss the major economic and institutional incentives and barriers faced by different

private- and public-sector decision-makers when considering plastics recycling.

2.2 Literature review on integrated MCDM methods

Opricovic and Tzeng (2002) have provided a compromise solution by comparing VIKOR

(VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multi criteria

Optimization and Compromise Solution) and TOPSIS method. The research compares the two

MCDM methods, VIKOR and TOPSIS by focusing on modeling aggregating function and

normalization, in order to reveal and to compare the procedural basis of these two MCDM

methods. Dagdeviren et al. (2008) have integrated AHP and TOPSIS under fuzzy environment

for the weapon selection process. This study proposed a systematic evaluation model to help the

actors in defence industries for the selection of an optimal weapon among a set of available

alternatives. Sun (2010) has proposed a model to evaluate the performance of global top four

notebook computer ODM companies. This study integrated two MCDM methods namely AHP

and TOPSIS under fuzzy environment. Fuzzy AHP was used to determine the preference weights

of evaluation. Then the fuzzy TOPSIS method was used to improve the gaps of alternatives

between real performances. Kaya and kahraman (2011) have integrated AHP and Elimination Et

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Choix Traduisant la REalit (ELECTRE) method under fuzzy environment to determine the

environmental impact assessment. They proposed an environmental impact assessment method

which was based on an integrated fuzzy AHPELECTRE approach in the context of urban

industrial planning. Ho et al. (2011) have combined decision making trial and evaluation

laboratory (DEMATEL) technique with a novel MCDM model for exploring portfolio selection

based on CAPM. They identified that the factors of the CAPM possessed a self-effect

relationship according to the DEMATEL technique. Kuo and Liang (2011) have combined

VIKOR with Grey Relation Analysis (GRA) techniques to evaluate service quality of airports

under fuzzy environment. They observed that this approach is an effective means for tackling

MCDM problems involving subjective assessments of qualitative attributes in a fuzzy

environment. Chen and Tzeng (2011) have created the aspired intelligent assessment systems for

teaching materials with Analytical Network Process (ANP) approach where weights are based on

DEMATEL technique. They have found that improvement in the efficiency and quality of the

authored Mandarin Chinese teaching materials may be extended to other learning Areas.

Chenayah et al. (2010) have proposed a qualitative multicritera decision aid by combining

ELECTRE and Preference Ranking Organization Method for Enrichment Evaluation

(PROMETHEE) methods. They have shown that in the case of multiple decision makers, by

comparing the ranking derived from the aggregated outranking relation matrix with one of each

decision making with different interest involved in the decision process, it can be observed that

how much they are close or different using using the eigenvector method towards the collective

decision. Salminen et al. (1998) have compared multicriteria methods in the context of

environmental problems. This study analyzed the use of ELECTRE III and PROMETHEE I, II

decision-aids in the context of four different real applications to environmental problems in

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Finland. They have concluded that it is better to use several methods for the same problem

whenever possible. Chen and Chen (2010) used a novel conjunctive MCDM approach based on

DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher

education. They proposed a novel innovation support system in which measurement criteria are

extracted from top six weights among all criteria. Hung (2010) has used an activity-based

divergent supply chain planning for competitive advantage in the risky global environment using

DEMATEL-ANP fuzzy goal programming approach. This analysis showed that identifying and

relaxing crucial constraints can play an important role in divergent Supply chain planning for

higher competitive advantage and lower risk.

Peng (2012) proposed an approach to assess the regional earthquake vulnerability by integrating

results obtained using different MCDM methods. The weights of several MCDM methods were

calculated using Spearmans ranking correlation coefficients. The MCDM method with highest

weight was trusted most and was used to provide final assessment by integrating other MCDM

methods. The method proved to produce a comprehensive assessment of regional earthquake

vulnerability. Gurbuz et al. (2012) proposed an evaluation framework which integrates MCDM

methodologies Analytic Network Process (ANP), Choquet Integral (CI) and Measuring

Attractiveness by a Categorical Based Evaluation Technique (MACBETH) used for evaluating

Enterprise Resource Planning (ERP) alternatives. The applicability of the framework was

illustrated using a case study of the ERP software selection of a company. Yeh et al. (2013)

found that both Critical Success Factors (CSF) and Key Performance Indicators (KPI) affecting

the outcome of New Product Development (NPD) project. The authors used Fuzzy Decision

Making Trial and Evaluation Laboratory (FDEMATEL) to identify the correlation among critical

factors and Analytic Hierarchy Process (FAHP) to establish weights to the factors that affect

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NPD. Moghimi and Anwari (2013) analyzed financial ratios of Iranian cement producers. The

authors proposed an integrated fuzzy approach in which fuzzy analytic hierarchy process

(FAHP) was used to determine the criteria, where as Technique for Order Preference by

Similarity to Ideal Solutions (TOPSIS) was used to determine the option rankings. The authors

suggested using this evaluation methodology to other sectors too. Zolfani et al. (2013) used two

MCDM methods for evaluating potential alternatives of locations for establishment of shopping

malls. Relative importance of criteria and weights were calculated using Stepwise Weight

Assessment Ratio Analysis (SWARA). Potential alternatives were evaluated using Weighted

Aggregated Sum Product Assessment (WASPAS). The methodology was found to be applicable

for choosing location alternatives in other business case studies. Kabak and Dagdaviren (2014)

proposed a hybrid model integrating Benefits, Opportunities, Costs and Risks (BOCR) and

Analytic Network Process (ANP) to determine energy status of Turkey and prioritize alternative

renewable energy resources. The methodology enabled taking precautions against the possible

risks. The model also helped in making effective decisions. Hu et al. (2014) developed a hybrid

MCDM model which combines DANP (DEMATEL-based ANP) and VIKOR. The method

prioritized the relative influence weights of dimensions and criteria. The method could handle

complex interactions and interdependencies thus facilitating the evaluation of various strategy

processes.

2.3 Research gap

Based on literature review, it is inferred that several researchers have attempted integrated

MCDM methods for several applications. But the usage of integrated MCDM in the context of

plastics recycling is found to be scant. Selection of the best plastic recycling method involves

complex decision variables. Since a single method is not sufficient to identify the best plastic

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recycling method, there exists a need to apply the integrated approach to solve this problem. The

case evaluates selection of best recycling method by evaluating using a set of twenty criteria.

This method solves problem in a fuzzy environment since both criteria and weights are vague in

nature. Furthermore, vagueness also exists in determining how each criterion impacts the

attributes for evaluation. In order to deal with vagueness and uncertainty associated with decision

making problem, fuzzy based methods are being used in the present study.

3. Methodology

This research aims to select the best method among various plastic recycling process using the

integrated AHP and TOPSIS techniques under fuzzy environment. Fuzzy AHP is used to

determine the preference weights of evaluation (Kaya and Kahraman 2011). Fuzzy TOPSIS is

used to improve the gaps of alternatives between real performance values and achieve aspiration

levels and to evaluate the best process based on the various characteristics of three plastic

recycling processes (Chen and Chen 2010).

3.1 Fuzzy AHP

AHP is a method which is used to solve complex decision problems by determining the relative

importance of a set of activities in a problem (Chenayah 2010). AHP method decomposes a

complex multi criteria decision problem into a series of interrelated decisions. However, the pure

AHP model has some shortcomings. AHP method is used in nearly crisp-information decision

applications; the AHP method creates and deals with a very unbalanced scale of judgment (Kaya

and Kahraman 2011); AHP method does not take into account the uncertainty associated with

the process involved. To overcome these problems, several researchers integrated fuzzy theory

with AHP to improve the uncertainty. Fuzzy AHP based on the fuzzy interval arithmetic with

triangular fuzzy numbers and confidence index a with interval mean approach to determine the

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weights for evaluative elements (Chen and Chen 2010). This research uses triangular fuzzy

numbers (TFNs) for the evaluation. The steps in Fuzzy AHP are presented as follows:

Step 1: Building the evaluation hierarchy systems for evaluating the best alternative among the

given alternatives considering the various criteria involved. The selection of best alternative will

be done based on building the hierarchical system.

Step 2: Determining the evaluation dimensions weights using Triangular Fuzzy numbers.This

research uses TFN for the pair wise comparisons and finds the fuzzy weights. The reason for

using a TFN is that it is intuitively easy for the decision makers to use and calculate. In addition,

modeling TFN has proven to be an effective way of formulating decision problems where the

information available is subjective and imprecise.

The computational process about fuzzy AHP is detailed as follows. A triangular fuzzy number a

can be defined by a triplet (a1, a2, a3). The membership function ~A(x) is defined. By

(Sun 2010)

Linguistic variables take on values defined in its term set: its set of linguistic terms. Linguistic

terms are subjective categories for the linguistic variables. A linguistic variable is a variable

whose values are words or sentences in a natural or artificial language. Here, we use this kind of

expression to compare two criteria comparing evaluation dimension using nine basic linguistic

terms, as Perfect, Absolute, Very good, Fairly good, Good, Preferable, Not

Bad, Weak advantage and Equal with respect to a fuzzy nine level scale. Each

membership function (scale of fuzzy number) is defined by three parameters of the symmetric

triangular fuzzy number, the left point, and middle point.

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Step 3: Determining the weights for the criteria involved. Determination of weights for

evaluation criteria involves the following steps:

a. The pair-wise comparison matrix showing the preference of one criterion over the other is

build by entering the judgmental values by the decision makers. Since the values are linguistic

variables a triplet of triangular fuzzy numbers are entered.

b. The synthetic pair-wise comparison matrix is computed using geometric mean method

Geometric mean ri defined as

ri = (aij1x aij

2 aij10)1/10 (Sun 2010)

Step 3: The weight for each criterion is determined. This is done by normalizing the matrix.

wi= rix ( r1+ r2+ r3 + +rn)-1

(Sun 2010)

Step 4: The Best Non-Fuzzy Performance (BNP) value for each weight is determined. BNP

value for a weight (l,m,u) is given by

BNP value = [(u-l) + (m-l)] / 3 + l (Sun 2010)

Step 5: The criteria are ranked based on the BNP values. The criterion having larger BNP value

is considered to have a greater impact when compared with other criterion.

3.2. Fuzzy TOPSIS

The primary concept of TOPSIS approach is that the most preferred alternative should not only

have the shortest distance from the positive ideal solution (PIS), but also have the farthest

distance from the negative ideal solution (NIS) (Dagdeviren et al. 2009). TOPSIS has a relative

advantage that only limited subjective input is needed from decision makers and the ability of the

method to identify the best alternative quickly. The steps involved in Fuzzy TOPSIS are

presented as follows

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Step 1: Obtain the weighting of criteria from Fuzzy AHP. The result of Fuzzy AHP contains the

weights of each criterion under consideration.

Step 2: Create Fuzzy evaluation matrix. The judgmental values from decision makers for each

decision alternative corresponding to each criterion are tabulated with TFNs as entries.

Step 3: Normalize fuzzy decision matrix. The normalized fuzzy decision matrix is denoted by R

whose elements are [ri]mn; i= 1,2 3m, where m is the total number of criteria.

(Sun 2010)

Where u+j is the maximum value in the entire fuzzy decision matrix

Step 5: Determine the fuzzy positive ideal and fuzzy negative ideal reference points. Fuzzy

positive ideal solution (FPIS) and fuzzy negative ideal solutions (FNIS) are defined by the area

compensation technique.

FPIS A+ = (vi+ vj+ vn+) (Sun 2010)

FNIS A-= (vi- vj- vn-)

and vi= (1,1,1) x (wj)

Step 6: The distance from FPIS (d+) and gap from FNIS (d-) are identified by.

(Dagdeviren et al. 2009)

Where d(a,b) is the distance between the two fuzzy numbers a and b. It is defined as

(Dagdeviren et al. 2009)

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Step 7: The relative closeness to the ideal value is determined and alternatives are ranked

accordingly. The relative closeness is given by

(Sun 2010)

4. Case Study

The case study involves the selection of best recycling method using the Fuzzy AHP-TOPSIS

technique. The case study of the proposed integrated model was applied in an automotive

component manufacturing organization located in Bangalore, India. The case organization

produces automotive components using plastics. To identify the best plastic recycling method the

integrated approach was used.

4.1 Plastic recycling process

The growth in automotive production has increased the number of vehicles that needs to be

recycled. The traditional approach for End-of-Life Vehicles (ELVs) option involves dismantling,

shredding and disposing in the landfill (Tien et al., 2014). The use of metals in automotive

manufacturing has drastically reduced in the last 30 years with more emphasis on use of plastic

components (Duval et al., 2007). The recycling of Fibre Reinforcement Plastics (FRP) in

automotive industry is the new challenge and automotive alliances are working for evaluating the

recycling options for automotive industries (Reinforced Plastics, 1998). 75% of ELVs total

weight is recycled and remaining 25% of Auto Shredder Residues (ASR) is disposed as landfill

(Nourreddine, 2007).

There existed a need for the case organization to recycle plastics. Lardinois et al. (1995) defined

recycling of plastics as the process by which plastic waste material that would otherwise become

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solid waste is collected, separated, processed and returned for further usage. There are three

major plastic recycling processes (Wienaah 2007). They are described below

4.1.1 Mechanical recycling process

Mechanical recycling involves the physical method of material reprocessing of waste

plastics into plastics products. The end products of consistent quality are generated by cleaning

and processing the sorted plastics. The post process of recycling depends on the kind of

operation, but usually involve inspection for removal of contaminants or further sorting,

grinding, washing and drying and conversion into either flakes or pellets.

4.1.2 Chemical recycling process

Chemical recycling or feedstock recycling involves the breaking action of polymeric product into

its individual components (monomers for plastics or hydrocarbon feedstock synthesis gas) and

broken components could then be fed back as input raw material to reproduce the original

product or others.

4.1.3 Energy recovery

Horrocks (1996) compared locked-in potential (LIP) in terms of calorific value for plastics with

conventional fuels. Association of Plastics Manufacturers in Europe (APME) (2002-2003)

reported that western European industries are increasingly using energy recovery as the method

of plastic recycling. Wienaah (2007) applied energy recovery as one of the method of plastic

recycling for waste management in Ghana.

Plastics can be co-burned with other wastes or used as substitute fuel in several industry

processes. Certain other thermal and chemical processes like pyrolisis could be used for

recovering the energy content of plastic waste. The generated plastic waste on continuous

recycling loses their physical and chemical properties at their end- of-life phase.

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4.2 Criteria

There are certain criteria that are involved in the plastic recycling process. The post effect of the

plastic recycling process is well studied and based on the literature 20 criteria have been

identified. These criteria were verified and validated by the decision makers of the case

organization. The decision makers possess rich experience regarding the working culture of the

organization and plastics recycling and the inputs are gathered in terms of linguistic variables.

They are Economic performance, Financial health, Potential financial benefits, Trading

opportunities, Air resources, Water resources, Land resources, Mineral and energy resources,

Internal human resources, External population, Stake holder population, Macro social

performance (Vinodh 2010). Managerial ability, New technology acceptance, Interest support

groups, Customer Satisfaction Technical support and training (Yuksel and Dagdeviren 2010),

Technical capability, Managerial effectiveness, Management ability (Yuksel and Dagdeviren

2007).

4.3 Computation using Integrated Fuzzy AHP-TOPSIS

Step 1: The pair-wise comparison matrix for the fuzzy-AHP process was filled based on the

discussion with the decision makers. Decision makers provided the linguistic variables. The

linguistic scales are transferred to the corresponding fuzzy numbers. Fuzzy numbers are used for

incorporating views of decision makers for the criteria. The pair-wise comparison matrix

showing the preference of one criterion over the other is built by entering the judgmental values

of the decision makers. This will enable to determine the weights of criteria. An excerpt of pair-

wise comparison matrix for Fuzzy AHP process is shown in the Table 1.

Insert Table 1

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Step 2: Fuzzy geometric mean is obtained. Each cell ri is described by Geometric mean

technique. That is

ri = (aij1x aij

2 aij10)

For example fuzzy geometric mean for the set (2, 3, 4) is given by

r = (1, 1, 1) x (1, 1, 1) . (2, 3, 4) 1/10 = (0.88, 1.14, 1.37)

The geometric mean is calculated by applying the above formula for values of decision makers

for each criterion. The other matrix elements are obtained by using the same computational

procedure as shown in Table 2

Insert Table 2

The values r1 to r20 refers to the Fuzzy geometric mean value. The values of fuzzy geometric

mean values for twenty criteria obtained by geometric mean calculation are a prerequisite to

determine the weights for twenty criteria. These values are used in the next step to calculate the

AHP weight for criteria.

Step 3: The weight of each dimension is calculated. Each cell w iis defined by

wi= rix ( r1+ r2+ r3 + +rn)-1

For w1= (1.81, 1.2, 1.216) x (20.098, 20.118, 20.136)-1 = (0.09, 0.06, 0.06). Similarly fuzzy

weights w1 to w20 are calculated. The weights are obtained for each criterion by dividing the

corresponding geometric mean with the sum of geometric means for all criteria.

The resulting weight is shown in Table 3.

Insert Table 3

The values w1 to w20 refers to the Fuzzy geometric mean value. These weights will enable the

prioritization of criteria. The weights determined using Fuzzy-AHP method is used for

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evaluating three alternatives i.e. chemical recycling, mechanical recycling and energy recovery

for the set of twenty criteria. This weight is used in Fuzzy-TOPSIS method.

Step 4: The fuzzy decision matrix for the three plastic recycling processes was filled by the

decision makers. The decision matrix is obtained for the three alternatives for set of twenty

criteria. These details are shown in Table 4.

Insert Table 4

This table shows the fuzzy decision values based on the impact of criteria on plastic recycling

methods. Each value in the cell aij describes the impact of ith criteria on the jthalternative

Step 5: Normalize the fuzzy decision matrix by taking the largest fuzzy number out of the

complete set and divide it with all the fuzzy set.

In our case study 10.0 is the largest value. So the normalized fuzzy decision matrix is obtained

by dividing all the fuzzy numbers with 10.0. The normalized matrix will be used for determining

the positive and negative ideal solutions for the criteria.

Step 6: Determine the fuzzy positive ideal solution and fuzzy negative ideal solution by

identifying cost and benefit criteria. If it is a cost criteria v+ is set assigned to (1,1,1) and v- is

assigned to (0,0,0) and if it is a benefit criteria v+ is assigned (0,0,0) and v- as (1,1,1).

Step 7: Identify the distance of each alternative (d+) and the gaps associated with it (d-) by area

compensation technique and closeness coefficient is calculated. This will enable in identifying

the distances of three attributes from the ideal solution.

When fuzzy set a = (2, 3, 4) and b = (5, 6, 7) then

D(a,b) = [ 1/3 x [(5-2)2+ (6-3)2+ (7-4)2] ] 1/3 = 2.08

Table 5 and 6 shows the d+ and d- values correspondingly.

Insert Table 5

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Insert Table 6

5. Results and discussions

The BNP values from Fuzzy AHP are shown in Table 7.

Insert Table 7

It can be seen from the ranking of criteria, that criteria 1 is economic performance has the larger

impact. Similarly the weights of criteria can be compared with each other to know the relative

importance. These weights are used in the Fuzzy TOPSIS method along with the Fuzzy decision

matrix to identify the best method for plastic recycling.

The closeness coefficient is as shown in Table 8.

Insert Table 8

It is found that closeness coefficient ranks in the order of mechanical recycling greater than

chemical recycling greater energy recovery. This shows that mechanical recycling process is best

suited for the case organization. Mechanical recycling is the material reprocessing of waste

plastics by physical means into plastics products. The sorted plastics are cleaned and processed

directly into end products or into flakes or pellets of consistent quality acceptable to be

manufactured (Wienaah 2007). The steps taken to recycle post-consumer plastics typically

involve inspection for removal of contaminants or further sorting, grinding, washing and drying

and conversion into either flakes or pellets. Efforts are taken by the case organization towards

implementation of the mechanical recycling method.

6. Conclusion

In the growing competitive industrial scenario, recycling has become a major process in

manufacturing industries (Chenayah 2010). Industries which manufacture plastic based products

have the necessity to recycle plastics. There are number of methods to recycle plastics (Wienaah

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2007). Since selecting the best recycling method involves complex decision variables, it is

considered to be an MCDM problem. In this context, a case study was performed to decide the

best plastic recycling process. The importance of the dimensions was evaluated by industry

experts, and the uncertainty of human decision-making was taken into account using the fuzzy

concept. In our study, integrated Fuzzy AHP-TOPSIS method was used. Mechanical recycling

process was found to be the suitable recycling process among the given alternatives. Fuzzy

TOPSIS has eliminated many procedures to be performed only in AHP-fuzzy AHP solution and

enabled the deviation of conclusion in a shorter time. Additionally, in the application, it was

shown that calculation of the criteria weights is important in TOPSIS method and they could

change the ranking. This case study enables the decision analysts to choose the suitable plastic

recycling method for their plastic manufacturing organization.

6.1. Limitations and Scope for future work

In the present study, integrated Fuzzy AHP-TOPSIS is used for selecting the best plastics

recycling process. In future, other techniques like Fuzzy ANP, ELECTRE, VIKOR and other

combination could be explored. The comparison of the results obtained from other integrated

techniques will help in better understanding of the criteria involved and in the selection process.

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References

Association of Plastics Manufacturers in Europe (APME), 2002-2003. Plastics in Europe-An

analysis of plastics consumption and recovery in Europe.

Automotive industry rises to challenge set by recycling, 1998, Reinforced Plastics, 42 (5), 40-42.

Chen J.K and Chen I.S., 2010, Using a novel conjunctive MCDM approach based on

DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese

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Figures and Tables

Table 1. Excerpt of pair-wise comparison matrix for Fuzzy AHP

r1 r2 r3 r4 r5 r6 r7 r8

r1 (1.000, 1.000, 1.000) (1.231, 1.246, 1.259) (1.149, 1.175, 1.196) (1.231, 1.246, 1.259) (1.149, 1.175, 1.196) (1.231, 1.246, 1.259) (1.149, 1.175, 1.196) (1.231, 1.246, 1

r2 (0.812, 0.803, 0.794) (1.000, 1.000, 1.000) (1.175, 1.196, 1.215) (1.215, 1.231, 1.246) (1.231, 1.246, 1.259) (1.215, 1.231, 1.246) (1.116, 1.149, 1.175) (1.149, 1.175, 1

r3 (0.871, 0.851, 0.836) (0.851, 0.836, 0.823) (1.000, 1.000, 1.000) (1.196, 1.215, 1.231) (1.149, 1.175, 1.196) (1.196, 1.215, 1.231) (1.215, 1.231, 1.246) (1.196, 1.215, 1

r4 (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (1.000, 1.000, 1.000) (1.149, 1.175, 1.196) (1.231, 1.246, 1.259) (1.175, 1.196, 1.215) (1.231, 1.246, 1

r5 (0.871, 0.851, 0.836) (0.812, 0.803, 0.794) (0.871, 0.851, 0.836) (0.871, 0.851, 0.836) (1.000, 1.000, 1.000) (1.196, 1.215, 1.231) (1.231, 1.246, 1.259) (1.215, 1.231, 1

r6 (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (0.812, 0.803, 0.794) (0.836, 0.823, 0.812) (1.000, 1.000, 1.000) (1.149, 1.175, 1.196) (1.231, 1.246, 1

r7 (0.871, 0.851, 0.836) (0.896, 0.871, 0.851) (0.823, 0.812, 0.803) (0.851, 0.836, 0.823) (0.812, 0.803, 0.794) (0.871, 0.851, 0.836) (1.000, 1.000, 1.000) (1.215, 1.231, 1

r8 (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.871, 0.851, 0.836) (0.871, 0.851, 0.836) (0.812, 0.803, 0.794) (0.812, 0.803, 0.794) (1.000, 1.000, 1

r9 (0.836, 0.823, 0.812) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.896, 0.871, 0.851) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.812, 0.803, 0.794) (1.231, 1.246, 1

r10 (0.812, 0.803, 0.794) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (1.000, 1.000, 1

r11 (0.823, 0.812, 0.803) (0.812, 0.803, 0.794) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.851, 0.836, 0.823) (0.812, 0.803, 0.794) (0.871, 0.851, 0

r12 (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.823, 0.812, 0

r13 (0.896, 0.871, 0.851) (0.823, 0.812, 0.803) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.823, 0.812, 0

r14 (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.896, 0.871, 0.851) (0.823, 0.812, 0.803) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.851, 0.836, 0.823) (0.823, 0.812, 0

r15 (0.871, 0.851, 0.836) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.896, 0.871, 0.851) (0.823, 0.812, 0.803) (0.896, 0.871, 0

r16 (0.812, 0.813, 0.794) (0.823, 0.812, 0.803) (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.871, 0.851, 0.836) (0.812, 0.803, 0

r17 (0.896, 0.871, 0.851) (0.851, 0.836, 0.823) (0.871, 0.851, 0.836) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.823, 0.812, 0

r18 (0.812, 0.803, 0.794) (0.851, 0.836, 0.823) (0.871, 0.851, 0.836) (0.836, 0.823, 0.812) (0.823, 0.812, 0.803) (0.812, 0.803, 0.794) (0.823, 0.812, 0.803) (0.896, 0.871, 0

r19 (0.836, 0.823, 0.812) (0.871, 0.851, 0.836) (0.851, 0.836, 0.823) (0.896, 0.871, 0.851) (0.823, 0.812, 0.803) (0.851, 0.836, 0.823) (0.871, 0.851, 0.836) (0.823, 0.812, 0

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Table 2. Fuzzy geometric mean for various criteria

r1 (1.181,1.200,1.216)

r2 (1.160,1.177,1.191)

r3 (1.126,1.142,1.156)

r4 (1.107,1.121,1.133)r5 (1.107,1.116,1.124)

r6 (1.079,1.088,1.095)

r7 (1.073,1.077,1.081)

r8 (1.042,1.046,1.050)

r9 (1.028,1.031,1.033)

r10 (1.001,1.004,1.005)

r11 (0.993,0.992,0.991)

r12 (0.960,0.960,0.960)

r13 (0.960,0.956,0.952)

r14 (0.944,0.938,0.933)

r15 (0.933,0.924,0.917)r16 (0.903,0.896,0.889)

r17 (0.902,0.890,0.881)

r18 (0.878,0.867,0.858)

r19 (0.865,0.852,0.842)

r20 (0.856,0.841,0.829)

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Table 3. Fuzzy weights of AHP process for various criteria

W1 (0.059,0.060,0.060)

W2 (0.058,0.058,0.059)

W3 (0.056,0.057,0.057)

W4 (0.055,0.056,0.056)

W5 (0.055,0.055,0.056)

W6 (0.054,0.054,0.054)

W7 (0.053,0.054,0.054)

W8 (0.052,0.052,0.052)

W9 (0.051,0.051,0.051)

W10 (0.050,0.050,0.050)

W11 (0.049,0.049,0.049)

W12 (0.048,0.048,0.048)

W13 (0.048,0.048,0.047)W14 (0.047,0.047,0.046)

W15 (0.046,0.046,0.046)

W16 (0.045,0.045,0.044)

W17 (0.045,0.044,0.044)

W18 (0.044,0.043,0.043)

W19 (0.043,0.042,0.042)

W20 (0.043,0.042,0.041)

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Table 4 Fuzzy decision matrix

Alternative

Criteria

Chemical recycling Mechanical recycling Energy recovery

1 (6.000,8.000,9.000) (6.000,7.000,8.000) (5.000,6.000,7.000)

2 (5.000,6.000,7.000) (6.500,8.500,9.500) (4.000,5.000,6.000)3 (4.000,5.000,6.000) (5.000,6.500,8.000) (7.000,8.000,9.000)

4 (7.000,8.000,9.000) (6.500,7.500,8.500) (5.500,6.500,7.000)

5 (6.000,7.000,8.000) (7.500,8.000,8.500) (5.000,6.000,7.000)

6 (4.500,6.000,7.500) (6.000,7.500,9.000) (6.500,8.000,9.500)

7 (6.000,7.000,8.000) (5.000,6.000,7.000) (6.000,7.000,8.000)

8 (5.500,7.000,8.500) (8.000,9.000,10.000) (5.000,6.500,7.000)

9 (6.000,7.000,8.000) (7.000,8.000,9.000) (7.500,8.000,8.500)

10 (5.000,6.000,7.000) (4.000,5.000,6.000) (5.000,6.000,7.000)

11 (6.000,7.000,8.000) (7.000,8.500,9.500) (4.000,6.000,8.000)12 (4.000,5.000,6.000) (4.500,6.000,7.500) (7.000,8.000,9.000)

13 (7.000,8.000,9.000) (5.500,6.500,7.500) (5.500,7.000,8.500)

14 (5.000,6.000,7.000) (8.000,9.000,10.000) (5.500,7.000,8.500)

15 (6.000,7.000,8.000) (5.000,6.000,7.000) (6.000,7.000,8.000)

16 (6.000,7.000,8.000) (4.500,6.000,7.500) (6.000,7.000,8.000)

17 (5.500,7.000,8.500) (7.000,8.000,9.000) (6.000,7.000,8.000)

18 (5.500,7.000,8.500) (6.000,7.000,8.000) (5.000,6.000,7.000)

19 (8.000,9.000,10.000) (5.000,6.000,7.000) (5.000,6.000,7.000)

20 (4.500,6.000,7.500) (8.000,9.000,10.000) (4.500,6.000,7.500)

Table 5. d+ values for various alternatives in Fuzzy TOPSIS

Alternative D+ value

Chemical recycling 0.517

Mechanical recycling 0.490

Energy recovery 0.520

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Table 6. d- values for various alternatives in Fuzzy TOPSIS

Alternative D- value (x10-5)



Energy recovery 2.13

Table 7 Crisp values of Fuzzy AHP weight for various criteria

BNP1 0.0596012

BNP2 0.0584602

BNP3 0.0567218

BNP4 0.0556857

BNP5 0.0554434

BNP6 0.0540603

BNP7 0.0535428

BNP8 0.0520009

BNP9 0.0512273

BNP10 0.0498816BNP11 0.0492954

BNP12 0.0477204

BNP13 0.0475242

BNP14 0.0466348

BNP15 0.0459688

BNP16 0.0445439

BNP17 0.0443005

BNP18 0.0431459

BNP19 0.0423953

BNP20 0.04

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Table 8 Closeness Coefficient for the alternatives

Alternative Closeness coefficient(x10-4

)



Energy recovery 4.1

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