Sustainable Location Identification Decision Protocol (SuLIDeP) For Determining the Location of Recycling Centres in a Circular Economy

Al Amin Mohamed Sultan a* and Paul Tarisai Mativenga b

aAdvanced Manufacturing Centre (AMC ), Fakulti kejuruteraan Pembuatan (FKP), Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia

bSchool of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom*Corresponding author. E-mail address: alamin@utem.edu.my

Abstract

Landfill restrictions on certain materials and products have provided the impetus to seek for a more sustainable utilisation of waste in a circular economy. These restrictions compounded with legislation and value factors necessitate an urgent solution to address the issue of carbon or glass fibre reinforced composite waste disposal. There is currently no mutual agreement on waste ownership among stakeholders. This study examined composite manufacturers in the United Kingdom and determined the waste volumes available within these companies. A new approach that combined mathematical modelling of supply chain complexity, centre-of-gravity method and K-Means algorithm was developed to determine the optimum location of third parties that could process waste for a number of supply chain providers. The paper is a presentation of new knowledge and proposes a scientific approach for identifying possible optimum locations for recycling centres. More significantly, this process could be used for clustering and reducing supply chains complexity to enable the setting up of multiple and optimally located recycling centres. The results have indicated that the approach could minimise carbon footprint and greenhouse gas emission associated with transporting cores or waste to processing centres. This work is of generic importance that could be implemented across other waste and aspects of a circular economy such as remanufacturing.

Keywords: Circular economy; Supply chain complexity; Centre-of-gravity; Composites, Recycling location; Clustering Optimisation

1. Introduction

1.1 The circular economy concept

A circular economy is a strategy towards sustaining and maximising the lifespan and value of resources in use, and then recovering and regenerating products and materials at the end of each service life (Singh and Ordoñez, 2015; WRAP, 2015). Viewed and proposed as a potential solution to the world's emerging resource crisis, circular economy includes a range of cycles whereby resources are used, and their value continually optimised in various possible ways (Gervásio et al., 2014). This idea however is not entirely new as it is associated with a range of existing concepts such as reverse logistics (Khor and Udin, 2012), cradle to cradle (Akdoğan and Coşkun, 2012), sustainable supply chains (Shaharudin et al., 2015) and industrial ecology (Favot and Marini, 2013).

In a circular economy, resources such as metals and minerals are captured and reused at the end of their lives; this leads to a reduction in the amount of waste (Ashby, 2016), establishes higher resource productivity, and delivers a more competitive economy (Preston,

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2012). Due to its capability in reducing the environmental impacts on the production and consumption of raw materials (WRAP, 2015), the circular economy approach may provide the solution for the composite waste issues and challenges specifically in the context of the UK.

1.2 Composite in a Circular Economy

Composite is a mixture of matrix (resin) and reinforcement (fibres) that results in a material with superior properties (Yang et al., 2012). Carbon fibre and glass fibre are examples of composite materials currently available for critical and demanding sectors such as aerospace, wind energy and automotive (Shuaib et al., 2015). The high demand for composite based products significantly benefits the economy, but at the same time gives rise to a major waste problem (Halliwell, 2006).

UK composite data reports an estimated 304,000 tonnes of waste generated in 2015 (Halliwell, 2006). This figure consisted of 80% end-of-life waste and 20% production waste. The composite end-of-life waste from aerospace has been projected to reach an estimated 719,000 tonnes in 25 to 30 years based on data on wind turbines (RenewableUK, 2013), commercial planes and military aircraft (Eurostat, 2014; Think Defence, 2012). Up to 2016, 98% of composite materials were disposed of in UK landfills (Job et al., 2016). The small uptake of composite recycling shows the magnitude of this issue. Environmental concerns of the disposal of composites have resulted in the introduction of legislations such as the Landfill Directive (1999/31/EC), the Framework Directive on Waste (2008/0241 (COD)), and Environmental Permitting Regulations 2007 (SI 3538). Since the success of the circular economy may depend on effective end-of-life waste management, it is thus critical to examine the duties and responsibilities of key players. As the task of managing end-of-life waste expands and becomes more urgent, the need for proper and fair delegation in accountability is even more necessary. Identifying the responsible stakeholders in managing the collection and treatment of composite waste is an integral element in the circular economy. Failure in this aspect would lead to the dumping of composite waste.

1.3 Waste Ownership Models in Contradiction

Extended Producer Responsibility (EPR), a product take-back scheme, identifies manufacturers responsible for managing their product after being discarded by users through an environmentally sound approach (Lindhqvist, 2000; McKerlie et al., 2006). EPR aims to promote environmental considerations from the product design stage. Deemed as a virtuous take-back approach, it has been implemented particularly among the Organisation for Economic Co-operation and Development (OECD) countries such as Germany, Sweden, and the UK (OECD, 2001). In EPR, responsibilities including the end-of-life management costs are borne by manufacturers (Hickle, 2014). Some however, contract third-parties referred to as producer responsibility organisations (PROs) to manage their assigned industrial wastes (Massarutto, 2014; Özdemir-Akyıldırım, 2015). The PROs are tasked with ensuring the disposal of the manufacturers’ products are disposed of in a manner that is environmentally responsible and compliant with existing legislations (Spicer and Johnson, 2004). However, there is no universal or standard agreement on the manufacturers’ responsibility for end-of-life waste. Sachs (2006) argues that the end-of-life responsibility should instead be borne entirely by end users who turned product into waste and not

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manufacturers who create beneficial products. Scheijgrond (2011) proposes the likely need for governments to take charge entirely. These contradictions indicate significant misalignments in the perception of responsibility for end-of-life waste or engineered products, with the issue needing clarification for the composite industry and production waste. Literature on composite take-back is very few and some of the aspects have begun to be addressed in recent authors’ studies (Mativenga et al., 2017a, 2017b). It is therefore cogent to investigate the issue further, with more research needed to understand the factors influencing composite recycling, and develop tools to support stakeholders in making evidence-based decisions.

1.4 Composite’s Recycling Centre

Commercial recycling centres available for recycling mainstream composite materials are currently limited in number. From an operational perspective, this could be attributed to the inefficiencies or failure of reverse supply chain management to provide sufficient amounts of composite waste for recycling to take place (Larsen, 2009; Oliveux et al., 2015; Yang et al., 2012). Various composite recycling methods are available to date such as biotechnological, chemical, electrochemical, fluidised bed, high voltage fragmentation, mechanical grinding and microwave pyrolysis. The outlines of those approaches have previously presented in authors’ work (Mativenga et al., 2017b).

Several businesses and corporations worldwide have embarked on recycling their composite manufacturing wastes. Globally, there are companies such as Mixt Composite Recyclables (France), Reprocover (Belgium), and Eco-Wolf (USA) (Oliveux et al., 2015). In the UK, there are companies such as Hambleside Danelaw Ltd (Scotland) and Filon Products (England) undertake the in-house recycling of their glass fibre reinforced plastic product waste. At the commercial level, ELG Carbon fibre (England) is possibly the world’s first and the only commercial carbon fibre recycling company involved in manufacturing composite waste recycling in the UK (Job, 2014) with the recyclates eventually marketed in milled, chopped and palletised forms (ELGCF, 2015). However, since the company’s operating capacity is limited, it could only undertake a set of amount of waste processing annually (Job, 2014). The presence of the various types of operating companies and recycling centres is evidence of the vast potential of composite waste recycling commercially, with recycling technology expected to be better improved and produce quality recyclates in the near future.

Siting a processing centre where the appropriate recycling process could take place is imperative towards enhancing the composite circular economy. To realise this, the identification of the magnitude of waste and location of these sources have to be accurately made. The motivation for this paper is to develop a decision tool for assessing optimum solutions for waste processing centres. This research is expected to positively impact the forward push and success of the circular economy and sustainable use of composite materials. By having a systematic approach to a decision making tool, it would aid the stakeholders in making a more wise decision not only in assessing the vital factors for a new recycling location but would also help to cluster the locations with the lowest complexity of the supply chain networks.

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2. Research Methods

To address aforementioned gap in composite waste literature, a structured questionnaire was developed to examine the critical success factors (drivers, sustainers, barriers, current practices and waste ownership) among composite manufacturers in the UK. A questionnaire-based survey was selected as the data collection method due to it being more commonly used for collecting original information from respondents (Zaki et al., 2015). The results enabled identification of the physical address of the composite manufacturers as well as the estimated volumes of waste. Given the information about the supply chain as obtained from the survey, the next challenge was to develop a methodology for determining the location of a third-party recycling centre for the composite waste.

The Centre-of-Gravity method was used to evaluate the centre of gravity location for waste in the UK, or its nation’s state. To further assess the most suitable centre of gravity locations for a cluster of supply chain domains, a new approach of network complexity was developed. This was inspired by earlier works of authors which modelled how product design, recycling technology and material complexity influenced recycling rates (Sultan et al., 2017). The UK was used as a case study to establish the vital generic decision tool. A group of various locations was modelled by geospatial clustering with supply chain complexity introduced to assess the network complexity for alternative scenarios. These were assessed towards a solution that could minimise the travel distance of end-of-life wastes and reduce greenhouse gas emission from transportation.

2.1 Questionnaire

A questionnaire was devised for the survey and question was asked on the amount of composite waste generated in companies in the UK identified as companies related businesses. Once the survey questionnaire was finalised, companies were contacted through telephone. This approach helped identify principal senior engineering stakeholders that dealt with composites. Companies also confirmed their willingness to participate in the written survey. Part of the manufacturer’s survey findings were used in this paper since the rest of the survey outcomes was on the composite circular economy models (Mativenga et al., 2017a, 2017b).

2.1.1 Sample population

A list of prospective companies was obtained from the Composites UK 2015 Directory, the trade body for the UK composite industry. There were altogether 1,500 companies registered in the directory; this total however represented businesses from mixed backgrounds i.e. the registry consisted not only manufacturers but also consultants, contractors, designers, and marketing. Since the study was focused on composite manufacturing firms, such companies had to be extracted manually from the directory and their business backgrounds carefully scrutinised. The list was further updated by the inclusion of other available private composite databases where business details such as business sector, company name and contact information were extracted. To expand the list an open search of the companies was conducted by using various internet search engines.

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The distribution of composite companies to be targeted in the survey encompassed both UK based businesses and multinational companies of various sizes operating in the country. The companies were mainly aerospace-based businesses; however a significant proportion also supplied automotive and motor racing, rail, and marine, building construction, and allied industrial segments. Also included were leading wind power and related energy infrastructure companies followed by small medium enterprise composite product manufacturers across the supply segments of leisure, safety, and municipal and transport. From the initial 219 identified by the profile as manufacturing companies, 62 were found to have insufficient involvement in composite based manufacturing to warrant participation in the survey. This thus provided the eventual sample size of 157 composite manufacturing-related companies.

2.2 The recycling facilities identification through the Centre of Gravity Method

The success of a composite circular economy requires the continuous existence of composite recycling centres. This study found 157 composite companies distributed throughout the UK as shown in Figure 1. Recycling centres should be located at strategic localities which are fairly accessible and convenient for manufacturers to forward their waste to. One of the most popular methods used to select a location for a single facility is the centre-of-gravity method (Chase et al., 1998). This method identifies geographic midpoint as a suitable location for a composite recycling centre taking into consideration the average coordinates for a set of points on a spherical earth. For instance, when several weights are placed at various points on a world globe which is allowed to rotate freely, the heaviest part of the globe will subsequently be pulled by gravity causing it to face downwards. The lowest point on the globe would be the geographic midpoint for all of the weighted locations.

This methodology considers the existing facilities (such as manufacturing companies and recycling centres), and the distances between them and the volumes waste to be transported. Other factors could be tailored to suit the research objectives (e.g. size of companies, recycling rates, and waste amount). The methodology involves the use of formula to compute the coordinates that correspond to the distance and volume criteria. The Centre-of-Gravity is calculated using the equation suggested by Chase et al. (1998). Each latitude and longitude in spherical coordinates system is converted into Cartesian coordinates (X, Y, Z) as shown in Figure 2 so the accurate distance could be assessed. The X, Y, and Z coordinates are then multiplied by the weightage and added together. A line could be drawn from the focal point of the earth out to this new X, Y, Z coordinates, and the point where the line crosses the surface of the earth is the geographic midpoint. This surface point is converted into the latitude and longitude for the midpoint. It is worth mentioning that the earth is assumed as a perfect sphere instead of an oblate spheroid.

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Fig. 1: Distribution of composite manufacturers throughout the UK (Mapline, 2016)

The postcodes of all the locations of declared 157 composite manufacturing companies in the UK are used first to obtain their latitude and longitude coordinates. These coordinates are then converted into radians as in equations 1 and 2.

lat ri=lat i π180

.......... (1)

lonri=loniπ18 0

.......... (2)

Where, latri= latitude of i-th location in radian, lonri= longitudinal of i-th in radian and lat i value degree for i-th location.

The latitude and longitude value in radians are subsequently converted to Cartesian X, Y and Z-coordinates using equations 3, 4, and 5. This is shown in Figure 2.

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Fig. 2: Spherical coordinate’s system conversion into Cartesian coordinates system

X i=RCos (lat ri ) sin(lonri) .......... (3)

Y i=RCos (l atri ) cos( lonri) .......... (4)

Zi=RSin ( latri) .......... (5)

Where, X i= X-axis in Cartesian coordinate for i-th location, Y i= Y-axis in Cartesian coordinate for i-th location, R = radius of earth, lat ri= latitude of i-th location in radian, and lonri= longitudinal of i-th in radian.

In this study, the calculations were made for two different cases: (1) All of the companies were treated as equally important, and (2) weightage was taken into consideration based on the amount of generated composite wastes. The generated waste volume of each company was recorded and used as weightage in the calculation. The larger the generated waste, the closer should be the generating company to the recycling centre.

The centre of gravity of weighted Cartesian coordinates of X, Y, and Z was computed using

equations 6, 7, and 8 respectively. Apart from a distance, the volume of waste as a fraction

of the total supply chain waste was assigned as weight.

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X c=∑i

n

x iwi

∑i

n

w i

.......... (6)

Y c=∑i

n

y iwi

∑i

n

wi

.......... (7)

Zc=∑i

n

ziwi

∑i

n

wi

.......... (8)

Where, X c= X-coordinate of centre of gravity, Y c= Y-coordinates of centre of gravity, Zc= Z-coordinates of centre of gravity,X c= X-coordinate of i-th location, y i= Y-coordinate of i-th location,z i= Z-coordinate of i-th location and w i= weightage (e.g. volume of wastes moved to or from i-th)

The Cartesian coordinate were then converted back to latitude and longitude for the centre

of gravity or midpoint of 157 companies using equations 9, 10, and 11.

Lon=arctan(x , y) .......... (9)

Hyp=√x2+ y2 .......... (10)

Lat=arctan (Hyp, y ) .......... (11)

Where Lon = longitudinal coordinate for the midpoint in radian, Lat = latitude coordinate for the midpoint in radian andHyp = hypotenuse of Cartesian coordinates in radian.The final latitude and longitude of midpoint were converted from radians to degrees using equation 12 and 13

Latm=Lat (180 ) / π ......... (12)

Lonm=Lon (180 ) / π ........ (13)

Where Latm= latitude coordinate of mid-point in degree. Lonm= longitudinal coordinate of mid-point in degree.

2.3 Minimum Criteria through Weighted Sum Method

In order to identify the best alternatives, the minimum aggregate value of total distance travelled and greenhouse gas (GHG) emission as criteria would be assessed and the shortest driving distance of the travelled waste from the companies to collection centre or

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recycling centre estimated. The GHG emission conversion factors are applied corresponding to the number and type of vehicles used as suggested by the Department for Business, Energy and Industrial Strategy (BEIS, 2016). The conversion factors are shown in Table 1. The particular distance and greenhouse gas emission are assessed using Distance-based method for transportation consistent with Greenhouse Gas Protocol (GHG Protocol, 2016). The multiple types of vehicles are possible until the total amount of waste could be transported.

Table 1: Emission Factors per-vehicle-per-tonne-km for GHG Conversion Factors (BEIS, 2016)

Vehicle type and Body Type

Gross Vehicle Weight (tonne)

Waste capacity(tonne)

kgCO2e per tonne-kmCO2 CH4 N2O Total

Heavy Duty Truck, Rigid >3.5-7.5 2.00 0.5523

5 0.00025 0.00632 0.55892

Heavy Duty Truck, Rigid >7.5-17 5.00 0.3632

1 0.00017 0.00416 0.36754

Heavy Duty Truck, Rigid >17 9.00 0.1690

5 0.00008 0.00194 0.17107

Heavy Duty Truck, Articulated >3.5-33t 12.35 0.1441

3 0.00003 0.00165 0.14581

Heavy Duty Truck, Articulated >33t 18.50 0.0805

4 0.00002 0.00092 0.08148

Van, Class I, Diesel < 1.4 0.64 0.64610 0.000066 0.00790 0.65407

Large Ferry Freight n/a n/a 0.38434 0.00015 0.00286 0.38735

The weighted sum method (WSM) is used to identify the best options since this method is the best known and simplest multi-criteria decision analysis (MCDA) (Naidu et al., 2014). This method could be applied by the decision maker when there are dependant variables or conflicting variables on their objectives. Figure 3 shows the formulation that could be used with steps to transform (a) initial data to (b) the normalised score of alternatives using the example of two criteria that used in this study

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W 1 ⋯ Wm

C1 ⋯ CmA1 S11 ⋯ S1m

⋮ ⋮ ⋮

An Sn1 ⋯ Snm

NW 1 ⋯ NW m

C1 ⋯ CmFinal Score

A1 NS11 ⋯ N S1m F1

⋮ ⋮ ⋮ ⋮

An NSn1 ⋯ NSnm Fn

(a) (b)

Fig. 3 (a) The initial score and weights formulation-matrix and (b) after normalised

Equation 14 was used to determine normalised score of alternatives in criteria. Equation 15 was applied to obtain normalised weights of criteria. Normalisation is necessary to allow mathematical operation using different units among the criteria. The best alternative for the minimum function of optimised criteria was obtained using Equation 16.

NSij=Sij

∑i=1

n

S ij

; i=1,2 ,…,n , j=1,2 ,…. ,m …. (14)

NW j=W j

∑j=1

m

W j

; j=1,2 ,…,m …. (15)

F i=∑j=1

m

NW j×NSij ;i=1,2,…,n …. (16)

Where:Alternative: Ai ; i=1,2 ,…,nCriteria j: C j ; j=1,2 ,…,mWeight of Criteria j: W j; j=1,2 ,…,mScore of Alternative i in Criteria j: Sij ; i=1,2 ,…,n , j=1,2 ,…,m Normalised Score of Alternative i in Criteria j: NSij ; i=1,2 ,…,n , j=1,2 ,…,mNormalised Weight of Criteria j: NW j ; j=1,2,…,mFinal Score of Alternative i: F i; i=1,2 ,…. ,n

2.4 The Supply Chain Complexity Measure

The measure of ‘supply chain-item-distribution’ quantitatively evaluates the uncertainty or difficulty in assessing and locating items (e.g. product or waste) in a supply chain network. A new proposal here is that the supply chain complexity is quantified by a parameter H, the set of individual visits that is required to collect waste from a location, assuming that the waste could be collected in a single trip. This is a reasonable concern in ‘just-in-time’ or ‘near just-in-time’ processing. The supply chain complexity is logically assumed to be a factor of the mass to be collected and the distance to be travelled to collect the waste. The concept is illustrated in Figure 4. The waste is of mass M1, M2, M3, M4 and M5 while D1, D2, D3, D4, and D5 are the shortest transportation distance of the waste to the recycling center. The complexity is introduced as a mathematical measure of the challenge of collecting the waste to a processing center considering the geographical distraction.

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Fig. 4. Supply chain complexity assessment conceptual diagram

Overall mathematical treatment here will represent the complexity of item distribution in supply chain network. The complexity of supply chain was modelled by equation 17, with K = -1 to get the final positive integer. This equation is based on the complexity equation that applied to the recycling by Dahmus and Gutowski, (2007). This new application was to measure the challenges to transport the waste to the processing centre. It involves two locations per trip (i.e. the collection point and the delivery point) where the binary approach considered in the equation and the logarithm base 2 is used. The distance between two points and the amount of waste transported are represented by W and D. As a whole, it would give the final value of H, in bits, the difficulty on logistic concern (i.e. travelled distance and waste volume).

W i=MiM total

……………. (17)

Di=LiR

……………. (18)

Where W i is the mass fraction of an item in a company that makes a total waste volume of the industry, M iis the actual weight in kilogramme (kg) of the waste and M total is the total mass in Kg in the waste in the supply chain.

Where Di is the distance ratio of a company to the recycling centre, Liis the item transportation distance in kilometre (km) and R is the maximum capped distance for the supply chain and in this case study which was taken as 1400 km for the UK.

Overall mathematical treatment here will represent the complexity of item distribution in supply chain network. The complexity H sof supply chain was modelled by equation 19, with K =-1 to get positive integer as the final answer.

Hs =K∑i =1

M

W i Di log( W i Di ¿…… (19)

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3. Results and Discussions

The result and discussions are organised by explaining the finding from the questionnaire survey in section 3.1 while the findings on recycling locations on several illustrative scenarios utilising the centre-of-gravity method are reported in section 3.2. Section 3.3 presents the result on supply chain complexity for all the scenarios to identity the least complex supply chain for the suggested centre. Section 3.4 details the findings on transportation carbon footprint and distance analysis: this will later be used as a check and balance to confirm the outcomes from Section 3.3 whereby the least complex supply chain should be reflected as having lowest gas emission and cost or distance.

3.1 Analyses of Questionnaire Survey

3.1.1 The composites manufacturers’ business scale

The survey was completed by 50 companies, corresponding to an overall response rate of some 32% (or 45% of required sample size). This figure does not include 16 companies which responded but eventually did not agree to participate due to their low level of operation, the company operations had ceased, confidentiality issues, and lack or no time to participate. This would have given an actual response rate of over 40% (or almost 60% of required sample size). The response rate was considered acceptable since this is a primary data collection exercise for a new research i.e. composites, and still in-range of the acceptable response rates as mentioned by Nawrocka and Parker (2009). The response rate of this research was deemed as better than previously published response rates in scientific studies on manufacturing survey with 12.3% (Newman and Sridharan, 1995), 13.0% (Riedel and Pawar, 1997) and 10.8% (Gilgeous, 1998). All the companies were from the UK with almost half of them (49%), according to the European Union definition, considered micro enterprises with 1-9 employees (49%). Other respondents were from small enterprises (11%), medium-sized business (38%), and large enterprises (2%) which were classified between 10-49, 50-249, and 250-999 employees respectively.

3.1.2 Current Composite Circular Economy Practices in the UK

An additional question item in the questionnaire revealed that 781 tonnes of composite waste were generated annually. Only eleven companies responded to this question which gave the average waste volume as 71 tonnes with the standard deviation of 132. Since the analysis in had considered all the 157 companies to produce viable results, the total waste of all the identified companies (including those that did not respond) was estimated based on the average amount of generated waste and their business size. This gave the new total waste volume at approximately 12,000 tonnes per year, with 76 tonnes on average and the standard deviation at 46.

From Figure 5, landfill disposal was the most common method for all composite waste i.e. carbon fibre, glass fibre, dry fibre, and resin. Although many companies in the UK deal with glass fibre only a fraction with carbon fibre. Of the respondents, 42% did not utilise carbon fibre in their manufacturing; they therefore selected either ‘Don’t know’ or ‘Other’ as their disposal method in the survey.

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Although reuse and recycle were the targeted methods, both indicated only small percentages (i.e. reuse of glass fibre at 6%, carbon fibre 2%, resins 6% and dry fibres 10%). The recycling rate was similarly recorded at 12%, 18%, 17% and 20% for glass fibre, carbon fibre, resins and dry fibres respectively. Incineration was indicated for glass fibres at 2% and resins at 19% respectively. The respondents also reported other means of disposal such as a specialist collection and curing on site until sufficient waste had been collected for proper disposal.

Landfill Re-use Recycling Incineration Don’t know Other0

10

20

30

40

50

60

70

80

90

100

35

2

20

0

13

30

67

613

2 48

30

7

18 16

7

23

46

11

22

07

15

Carbon Fibre Glass Fibre Resins Dry Fibre

End-of-life Routes

Perc

enta

ges (

%)

Fig. 5. Distribution of current composites waste management practices

3.2 The Centre of-Gravity Method Approach for Proposing Recycling Facilities

Several alternative scenarios for recycling centres were considered based on geospatial clustering: (1) new recycling facility for the entire UK, (2) existing recycling centre for the entire UK, (3) new recycling centres in every UK region (including England, Wales, Northern Ireland and Scotland), (4) new recycling centre for the entire UK and Northern Ireland with multiple collection centres at every UK region. Calculations were performed using MATLAB and equations 2 to 14, and the weightage were the amount of composites waste produced annually by a particular company.

The final output considering only the distance for Latm and Lonm was 52.5592 and -1.5042, respectively pinpointed the location as Woodford Lane (Nuneaton CV10 0SA, UK). The output of the calculation by considering the amount of generated waste by each company was 52.631801 and -1.569206 for latm and lonm, respectively thus indicating the location as Warton (Atherstone CV9 2LA, UK). This is shown as M in Figure 6. The distance between these two places was approximately 4 miles. This is indicative that the location of the waste, not the volumes, dominates the centre of gravity assessment and the logic to site recycling centers.

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Fig. 6. The geographically weighted recycling area shown by M (GeoMidpoint, 2016)

The location identification for alternatives; A, B, C and D, was completed and the results tabulated in Table 3. A, B and D were encompassed all 157 companies in the entire UK and Northern Ireland while C1, C2 and C3 were the regional assessment (i.e. England, Wales, Northern Ireland and Scotland). To ensure the best alternative, the Supply Chain Complexity was used, and the findings compared with Weighted Sum Method.

Table 3: Alternative clustering for proposed composites recycling centre

Alternative ClassificationRecycling

Facility / Collection Centre

Number ofcompany

ProposedLocation based on Centre of

Gravity MethodA The Entire UK and

Northern IrelandNew Recycling Center 157 Atherstone CV92LA

B Existing Recycling Center Coseley WV14 8XR

C By Country Number ofcompany

ProposedLocation

C1 England

New Recycling Center

139 Rugby CV23

C2 Wales 9 Llanidloes SY186JZ

C3 Northern Ireland 3 Belfast BT127GJ

C4 Scotland 6 Forfar DD81UL

D The Entire UK and Northern Ireland

12 Collection centres at every UK regionals (including Nothern Ireland, Scotland and Wales) with one recycling centre at Atherstone (CV93NL)

157

Loughborough (LE111PR), Newmarket (CB89PJ), Hounslow (TW59RZ), Stanley (DH96SH), Bolton (BL6), Forfar (DD81UL), Farnborough (GU148XQ), Bristol (BS45DY), Rhayader (LD65HS), Wednesbury (WS10), Leeds (LS103UB), Belfast (BT127GJ)

3.3 Supply Chain Complexity Measure

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The supply chain complexity analysis was applied for several clustering alternatives based mainly on the scenarios from the Table 3. The results summary is presented in Table 4. The complexity for scenario A and B were 0.98 and 1.38 (lower score is better). Both scenarios could be treated as initial points since there was no clustering yet at this stage. The score was thus solely influenced by the location of the recycling centre. This supply chain complexity was reduced significantly once the clustering had been done with, and the score 0.31 and 0.36 for 12 and 4 clusters respectively.

Table 4: Supply Chain Complexity for Composite’s Manufacturing Waste in the United Kingdom

Alternative

Classification

Number of

company

Waste Volume (tonne)

Clusters

Supply Chain

Complexity Index

A The Entire UK and

Northern Ireland

157 11928.82

No 0.98B No 1.38

CUK 4 0.36

D 12 0.31

The internal complexities for each cluster in CUK (Integration clusters of C1, C2, C3, C4) were calculated and presented in Table 5. The complexity for C1 (England) was 1.12, higher than the complexity of the UK in scenario A due to the randomness of travelled distance from each company to the recycling centre in England. Internal clusters complexity for C2-Wales, C3-Northern Ireland and C4-Scotland were 0.52, 0.08 and 0.47 respectively. This means the complexity of the UK was lowered by operating in regional cluster mode with the waste supply successfully coordinated within that particular region.

Table 5: Clusters Internal Supply Chain Complexity

Alternative

Classification

Number of

company

Waste Volume (tonne)

Clusters

Supply Chain

Complexity

C1 England 139 10639.52

No

1.12

C2 Wales 9 518.2 0.52

C3Northern Ireland 3 246 0.08

C4 Scotland 6 465.1 0.47

The set of alternatives illustrated in Figure 7 shows the clustering and supply chain complexity scores for alternatives A (the entire UK and Northern Ireland), C1 (England), C2

(Wales), C3 (Northern Ireland), C4 (Scotland) and CUK (Combination of C1, C2, C3, C4). Based on this figure, the supply chain complexity analysis for alternative A (where each company would forward their waste to one proposed recycling centre), was determined as 0.98. As predicted, this value was significantly reduced to 0.36 when the companies were assigned to four clusters (i.e. England, Wales, Scotland, and Northern Ireland) with the group of

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companies able to coordinate the waste collection among them before transporting to the recycling centre, as in alternative CUK.

Fig. 7. Different Alternatives for Clustering to reduce supply chain complexity

It is worth mentioning that the internal complexities for each cluster were determined using supply chain complexity measure with complexity scores for cluster C1, C2, C3 and C4 at 1.12, 0.52, 0.08 and 0.47 respectively. As demonstrated here, cluster C1 had the highest supply chain complexity score. This was based on a single centre in England where all of the companies should transport their waste for recycling. The examples of calculation for alternative clusters CUK (four clusters) and C2 (internal cluster’s supply chain complexity) were tabulated in Table 6 and Table 7.

Table 6: Example of calculation supply chain complexity of Alternative C = 4 clusters

Clusters Waste (tonne)

W i=M iM total

Distance to

Recycling Centre (km)

Di=LiR

W iDi logW iDi W iDi logW iD

England 10639.52 0.90 45 0.03 0.03 -5.12 0.15Wales 518.2 0.04 199 0.14 0.01 -7.33 0.05N.Ireland 246 0.02 624 0.45 0.01 -6.76 0.06Scotland 465.1 0.04 625 0.45 0.02 -5.84 0.10

H s 0.36

Table 7: Internal Complexity of Wales Cluster (C2)

Company Waste (tonne)

W i=M iM total

Distance to

Recycling

Di=LiR

W iDi logW iDi W iDi logW iDi

16

Centre (km)

1 93.00 0.18 100.00 0.07 0.01 -6.29 0.082 0.10 0.00 110.88 0.08 0.00 -16.00 0.003 0.10 0.00 124.96 0.09 0.00 -15.83 0.004 60.00 0.12 101.44 0.07 0.01 -6.90 0.065 93.00 0.18 85.28 0.06 0.01 -6.52 0.076 1.00 0.00 124.96 0.09 0.00 -12.50 0.007 85.00 0.16 102.40 0.07 0.01 -6.39 0.088 93.00 0.18 166.40 0.12 0.02 -5.55 0.129 93.00 0.18 161.60 0.12 0.02 -5.59 0.12

H s 0.52

The best alternative was chosen based on the least supply chain complexity among the assessed alternatives. On overall, the lowest supply chain complexity for the entire UK was 0.31 with 12 clusters as in Table 4. This result could be compared with the weighted sum method in section 3.4 to confirm the lowest transportation emissions and distance travelled.

3.4 Re-check through Weighted Sum Method

A Weighted Sum method was used to re-check the results from the least supply chain complexity in section 3.3 ensure it was reflected with reduced total transportation gas emissions and cost in terms of transportation distance and greenhouse gas/carbon footprint, the two attributes considered in rechecking the findings of supply chain complexity. The values for both attributes for rechecking were calculated based on final scenarios as presented in Table 7. These were then normalised to allow mathematical operation. The weighted sum method requires weight to be considered to the attributes; therefore the attributes here were treated as equally important. Each attribute was subsequently multiplied by weight which resulted in the final scores of the weighted decision matrix and the outputs ranked in ascending order since minimisation of attributes function was the aim. The treatments toward the final positioning are shown in Table 8. The final scores were listed in ascending orders of D, C, A, and B. This indicates that alternative D with the lowest final score was the option that minimised both total transportation distance and GHG emission.

Table 8: The weighted sum method calculation for multi-objective optimisation

Alternative

Decision Matrix(Criteria)

Normalised Decision Matrix

NormalisedWeights

Weighted Decision Matrix Final

Score

FinalRank

Total Transportatio

n Distance (TTD) (km)

Total Carbon Footprint

(TCF) (kgCO2e)

TTD TCF TTD TCF TTD TCF

A 31,412.84 36,168 0.304 0.167

0.500 0.500

0.152 0.083 0.235 2B 32,528.84 37,342 0.315 0.172 0.157 0.086 0.244 3C 24,986.92 28,046 0.242 0.130 0.121 0.065 0.186 1D 14,368.32 1,548,906 0.139 0.531 0.070 0.266 0.335 4

From the analyses, the Complexity Supply Chain measure is evidently reliable to assess the supply chain complexities for both intra-cluster and inter-cluster. This would give insightful view for stakeholders in designing vital supply chain networks for recycling centre locations. The result from the supply chain complexity method is feasible and could be ascertained by

17

the weighted sum method for reduced transportation distance and gas emission. As seen in Table which presents the entire UK scenario, the cluster that had the least supply chain complexity value and confirmed as having reduced total transportation distance and gas emission was D. This proves that the supply chain complexity measure in this research is applicable and consistent in predicting the most appropriate choice(s) while at the same time considering other critical factors for environment in supply chain such as transportation distance and carbon footprint.

In this paper, the case is illustrative because of no guarantee that all the companies with composites waste replied the questionnaire. Thus, the average weight based on the size of the business was assigned to those do not reply. If this methodology is going to be applied to the proper area in the future, then the real data is needed. Any commercial institutions interested to use this methodology; they should refine and validate the real data for accuracy of the results.

4. Optimised number of clusters

Centre-of-gravity method was used to locate the recycling centre based on the geo-spatial segmentation of the location based on county or municipal city as in the previous section. In this case, there is a possibility where the waste supplier (company) would be located at the border of the region/county or close to the border which may be closer to the recycling centre for a different county. Thus, another clustering alternative beyond the county or municipal restriction should be identified which will allow better management of the waste supply chain. This may need a proper delegation and planning based on the different council requirements but it might be more efficient than the previous approach since now the closest distances without any border restriction among the companies are considered.

Clustering algorithm is essential to solve this kind of problem. Several types of clustering models are available such as connectivity models, density-based clustering, centroid models and distribution models (Tan et al., 2006). Table 9 shows these models and their features. Of these methods, k-means that is based on centroid models would be appropriate for the location clustering.

Table 9: Types of cluster models (Tan et al., 2006)

Cluster Models

Example of models

Approach/Description of the models

Connectivity models

Hierarchical Builds models based on distance connectivity, these algorithms do not provide a single partitioning of the data set.

Density-based clustering

DBSCAN It assumes clusters of similar density, and may have problems separating nearby clusters.

Centroid models

k-means algorithm It represents each cluster by a single mean vector where the algorithms assign an object to the nearest centroid.

Distribution models

Expectation-maximization algorithm.

Clusters are modelled using statistical distributions (i.e. multivariate normal distributions). These algorithms put an extra burden on the user: for many real data sets, there may be no concisely defined mathematical model.

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4.1 K-means clustering algorithm

The K-means algorithm is a well-known partitioning method for clustering (Arora et al., 2016). K-means clustering method groups the data based on their closeness to each other according to the Euclidean distance. It takes initial number of clusters as input parameter and partition a set of locations. The mean value of the object is taken as similarity parameter to form clusters. The cluster mean or centre is formed by the random selection of number of cluster. By similarity other objects are assigned to the cluster. For each location this algorithm calculates the distance between location and each cluster centroid. The basic steps in k-means is given as follows (Geetha et al., 2009)

1- Choose the number of clusters, k, to process waste in a given supply chain boundary

2- Randomly generate k clusters and determine the cluster centres, or directly generate

k random points as cluster centres.

3- Assign each point to the nearest cluster centre

4- Re-compute the new cluster centres and

5- Repeat (3) until some convergence criterion is met or the cluster centres have not

changed.

4.2 Problem definition

In order to find optimum clusters for recycling centres, the waste detail (i.e. coordinate and volume of waste) are essential. In this case it was considered that the problem assessment is for England with n production waste supplier (number of companies), whose waste volume is known and distributed in spherical coordinates (later were converted to Cartesian coordinates). The n waste suppliers are grouped to form k clusters. Each cluster has n1,n2…,nk number of waste suppliers with the condition that

∑j=1

k

n j=n (20)

where n is the total number of waste suppliers. The problem is given with a set of

Waste suppliers points: r1,r2,r3…rn

Coordinates: (x1,y1), (x2,y2), (x3,y3)…., (xn,yn)Demands: d1,d2,d3…dn

Capacity: Cmax (maximum) and Cmin (minimum)

Where ri ∈ R are the set of waste r suppliers who are distributed in the Euclidean plane (xi,yi), the demand (di) and capacity of cluster are positive integers.

Let X be the binary matric, such that

x ij={1 if waste suppliersi is assigned¿cluster j ,¿1¿0otherwise¿

19

Consider an example of waste of a number of companies with production waste that needs to be allocated to clusters based on the capacity constraint of a typical composite recycling plant. Let C1, C2….,C10 be the companies distributed in (x,y) plane. The objective is to find X which minimises

∑j=1

k

∑i=1

n

distanceij∗x ij (21)

Subject to

∑j=1

k

x ij=1 , i=1,2 ,…,n (22)

∑i=1

n

d i x ij<Cmax , j=1,2,…,k (23)

∑i=1

n

d i x ij>Cmin , j=1,2 ,…,k (24)

where distance ij represents the closeness of company i to the cluster j (i.e. distance of travel between i and j). The objective function (Equation 21) strives to minimise the total travelling distances of waste suppliers to the cluster centre. The constraint (Equation 22) ensures that each company i is assigned to only one cluster j. The constraint (Equations 23 and 24) is to restrict that the total waste of the company in the cluster within the cluster maximum and minimum operating capacity, Cmax and Cmin. In this problem, Cmax is taken as 2,000 which was based on the limit of the currently operating composite recycling plant in the UK. The minimum operating capacity, Cmin was set at 1,000 tonnes (Equation 24).

Euclidean distance (distance ij) Equation 25 is used in calculating the distance between the company and the centre. Initial number of cluster could be started from the smallest number of cluster (e.g. 1 clusters).

distance ij=√(x i−x j)2+( y i− y j)

2 (25)

Where i=1,2 ,…,n

j=1,2 ,…,k

There are 137 composite manufacturing companies considered in England and the optimal number of cluster could be determined using k-means algorithm. The steps in calculating clusters are given as follows:

1. Convert all the longitudinal and latitude into Euclid form using Equations (3) to (6) so

the distance between locations could be found.

2. Set initial number of cluster, K from the smallest number of cluster (k=1)

3. Compute distances between each of the cluster point and all other points using

Equation (20).

20

4. Assign each case to the cluster having the closest mean and calculate the cluster

centres using centre of gravity.

5. Compute the overall supply chain complexity for clusters using Equation (19).

6. Go to step (3) until the algorithm converges (re-calculating distances and reassigning

cases to clusters until results in no change).

7. Calculate the total waste of each group.

8. If the total waste for each groups < 2,000 (waste centre processing capacity) and >

1,000 (waste centre minimum tolerable operating capacity) then stop, if not increase

the number of clusters (k = k + 1) and go to step (3).

4.3 Computational results

137 composites manufacturing companies in England were assessed using MATLAB processing software. The processing limit of each cluster (recycling plant) was set at maximum 2,000 tonnes and minimum 1,000 tonnes per year based on the currently operating capacity of the composite recycling plant. The results were tabulated in the Table 10. Only k=7 first meet the conditions while the rest of the cluster cases (k=1 to k= 6) exceeded the capacity of recycling centre. Thus, the result for optimised number of clusters for England was seven clusters to manage the supply chain of 10,461.52 tonnes of total waste.

Table 10: Information on different number of clusters

Clusters K=1 k=2 k=3 k=4 k=5 k=6 k=7Cluster 1 10461.5

24594.5

03040.6

22879.8

23082.2

02329.6

01238.2

0Cluster 2 5867.0

24594.5

02349.5

01654.3

02066.8

21488.3

0Cluster 3 2826.4

02676.8

02211.3

01764.0

01330.7

0Cluster 4 2555.4

01488.3

01393.7

01398.4

0Cluster 5 2025.4

21609.2

01654.7

0Cluster 6 1298.2

01742.1

2Cluster 7 1609.1

0

Figure 8 shows the distribution of the companies when all the conditions were met at seven clusters. The companies were arranged to be at the nearest processing centres.

21

-5 -4 -3 -2 -1 0 1 2Longitude

49

50

51

52

53

54

55

56

Latit

ude

Cluster Assignments and Centroids

1

2

3

4

5

6

7

Cluster 1Cluster 2Cluster 3Cluster 4Cluster 5Cluster 6Cluster 7Centroids

Fig. 8. Distribution of clusters and companies when k=7

The supply chain complexity of the clusters were assessed for each case as in Table 10 and the results are shown in Figure 9. The overall supply chain complexity was decreasing when the number of cluster increased until the clusters met the requirement of the cluster’s capacity. The complexity index of 0.78 (for two clusters=2) was reduced to 0.37 (for seven clusters) and this would be the lowest supply chain complexity where all the conditions were met.

K1 K2 K3 K4 K5 K6 K70.3

0.4

0.5

0.6

0.7

0.8

0.9

10.92

0.78

0.63

0.54

0.46

0.37 0.37

NUMBER OF CLUSTER (K)

SUPP

LY C

HAIN

CO

MPL

EXIT

Y, (H

)

Fig. 9. Supply chain complexity based on different clusters

22

Apart from proximity to raw material sources and transportation carbon footprint, several other factors may require attention such as (1) cost and availability of land, (2) cost of energy, (3) availability of transportation, (4) cost of transportation, (5) cost of raw materials, (6) availability of local labour and worker attitude, (7) cost of labour, (8) availability of managerial and technical personnel, (9) proximity to market, (10) government policies and incentives, (11) tax rates, (12) adjacent or neighbouring industries, (13) community environment, (14) availability of utilities (water and electricity), and (15) environmental conditions (Rao, 2007).

Those factors may have different levels of importance based on the perspectives of the investors, owners, stakeholders, government or society. Identified factors other than the ones focused on in this study could be further engaged by addressing them based on their identified or scaled importance. A related type of score or measure subsequently could be developed and applied to the calculation to assist in making informed decisions regarding plant location or site. The K-means algorithm used in this study was a vector quantisation clustering method that does not explicitly use pairwise distances between data points. It amounts to repeatedly assigning points to the closest centroid thereby using only Euclidean distance from data points to a centroid because the sum of squared deviations from the centroid is equal to the sum of the pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry. This would be the only possible way for the optimisation of clusters to be executed using the K-Means algorithm. In the future, the research could be taken forward by adopting a different approach for the algorithm utilisation especially the method that considers real distances as an input for multi-reiteration before the optimisation result converges.

Figure 10 illustrates the Sustainable Location Identification Decision Protocol (SuLIDeP) that could be used to determine the recycling location. Those steps could be used as a comprehensive decision strategy for any similar scenario as presented in this paper as was utilised in in this study (section 3.2 until section 4.3).

23

Fig. 10. Sustainable Location Identification Decision Protocol (SuLIDeP)

It was noted that several assessments were unified into a comprehensive strategy that acted as a decision tool for selecting the location for the new composites recycling plant. The assessments that were developed and utilised in this study were: i) processing centre assessment using centre-of-gravity method, ii) assessment on supply chain complexity, iii) clustering the locations, iv) assessments on total transportation distance and carbon footprint, v) weighted sum method and vi) k-means algorithm for optimised number of clusters. This unified suggested approach would be a tool for stakeholders in deciding the more optimum location for future processing centre(s), as the existing processing centre was unable to accept more waste due to the limitation of their operating capacity.

Previously, there was limited information on the composite manufacturing waste and the waste supplier magnitude. These prevented more significant study to take place regarding the recycling location. However, by determining the waste amount and the waste supplier locations in this study, a progress towards a new phase of research was possible where a further step in understanding the decision tool and the components was done. By having this

24

strategy the decision makers would be advised to not only be based on the logistics issue but also taking into account the sustainability matters such as carbon footprint and greenhouse gas emissions from the transportation.

This unified strategy was a reliable tool for decision-making as the application was proven with the related assessment results as shown in the analysis. However, it is undeniable fact that there was always room for improvement in terms of strengthening the suggested approaches. For example, other factors could be considered in the future, provided that the reliable data are available and accessible.

The case in this research is illustrative since not all the companies with composite waste responded to the questionnaire. In view of this, the average weights based on the size of the businesses concerned were assigned to non-respondents. If this methodology is to be applied to proper areas in future, real data would be necessary. Any commercial institution interested in using this methodology should first refine and validate real data for better accuracy of results.

5.0 Concluding Remarks

Decision-making strategy are crucial to the success of a circular economy especially recycling on composite waste. This research has set a new agenda that would assist in determining the optimum locations for the processing of waste for a number of supply chain providers. An alternative method that minimises complexity of waste collection (as measured by volume and distance), carbon footprint and greenhouse gas emission associated with transporting waste to processing centres was suggested. These generic work processes were presented as the Sustainable Location Identification Decision Protocol (SuLIDeP). It would help the stakeholders to optimise the decision strategy in assessing the vital factors for identifying a new recycling location by reducing the complexity of the whole supply chain network.

Several significant outputs could be derived from the results:

Currently, landfill disposal dominates end-of-life routes for composites for almost 90% of the composites.

Several locations based on specific scenarios for recycling centres and collection centres were proposed by utilising the centre-of-gravity method. The results of the new and existing single or multiple operating facilities for the entire UK or by UK regions (including England, Wales, Scotland and Northern Ireland) were modelled and presented. This methodology could be replicated in other scenarios.

A new supply chain complexity measure was developed and applied to assess the overall supply chain for waste taking into account the location, volume of waste and its relative position to a central processing point.

The travelled distance and greenhouse gas emission for transportation are reduced once the supply chain complexity is minimised.

The centre of gravity method and supply chain complexity assessments could be used to determine the location of recycling centres. The new approach developed in this paper is important due to its ease of replication or application to any

25

geographical location, different types of waste, and different aspects of the circular economy such as identifying optimum locations for recycling or remanufacturing businesses.

The total amount of generated waste was based on the estimated average waste deduced from business sizes of the responding companies.

Another significant element of this study is the indication that the new methodology allows the clustering of waste collection zones in order to reduce collection complexity. This however requires further investigation taking into consideration the maximum volumes to be transported, the types that could be transported, and the capacity of recycling centres.

Clustering algorithm such as k-means could be applied to cluster and further reduce the distances among companies to the identified centres which directly reduced complexity of the waste in the overall supply chain network. It would be useful to cluster locations with disregards the local authority’s restrictions such as different districts, municipals, counties or even countries.

Acknowledgement

The first author acknowledges the Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Education Malaysia for the doctoral studies scholarship. Special thanks to all the participating companies in the United Kingdom and the senior managers that dedicated their time to complete the questionnaire.

References

Akdoğan, M.Ş., Coşkun, A., 2012. Drivers of Reverse Logistics Activities: An Empirical Investigation. Procedia - Soc. Behav. Sci. 58, 1640–1649. https://doi.org/10.1016/j.sbspro.2012.09.1130

Arora, P., Deepali, Varshney, S., 2016. Analysis of K-Means and K-Medoids Algorithm for Big Data. Phys. Procedia 78, 507–512. https://doi.org/10.1016/j.procs.2016.02.095

Ashby, M.F., 2016. The Vision : A Circular Materials Economy, in: Materials and Sustainable Development. Elsevier, p. Pages 211–239. https://doi.org/10.1016/B978-0-08-100176-9.00014-1

BEIS, 2016. 2016 Government GHG Conversion Factors for Company Reporting : Methodology Paper for Emission Factors. London.

Chase, R.B., Aquilano, N.J., Jacobs, F.R., 1998. Production and Operations Management: Manufacturing and Services, 8th ed. Irwin and McGraw-Hill, Boston.

Dahmus, J., Gutowski, T., 2007. What Gets Recycled: An Information Theory Based Model for Product Recycling. Environ. Sci. Technol. 41, 7543–7550. https://doi.org/10.1021/es062254b

ELGCF, 2015. Converting Composite Waste Into High Quality Reusable Carbon Fibre.

Eurostat, 2014. Commercial Aircraft Fleet by Type of Aircraft [WWW Document]. Eurostat Database, Eur. Union. URL http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=avia_eq_arc_typ&lang=en

Favot, M., Marini, A., 2013. A Statistical Analysis of Prices of Electrical and Electronic

26

Equipment after the Introduction of the WEEE Directive. J. Ind. Ecol. 17, 827–834. https://doi.org/10.1111/jiec.12063

Geetha, S., Poonthalir, G., Vanathi, P., 2009. Improved K-Means Algorithm for Capacitated Clustering Problem. INFOCOMP 1, 8.

GeoMidpoint, 2016. Geographic Midpoint [WWW Document]. GeoMidpoint.com. URL http://www.geomidpoint.com/ (accessed 8.8.16).

Gervásio, H., Rebelo, C., Moura, a., Veljkovic, M., Simões da Silva, L., 2014. Comparative life cycle assessment of tubular wind towers and foundations – Part 2: Life cycle analysis. Eng. Struct. 74, 292–299. https://doi.org/10.1016/j.engstruct.2014.02.041

GHG Protocol, 2016. Technical Guidance for Calculating Scope 3 Emissions, Category 4: Upstream Transportation and Distribution [WWW Document]. Greenh. Gas Protoc. , World Resour. Inst. URL www.ghgprotocol.org/files/ghgp/Ch4_GHGP_Tech.pdf (accessed 10.26.16).

Gilgeous, V., 1998. Manufacturing managers: Their quality of working life. Integr. Manuf. Syst. 9, 173–181.

Halliwell, S., 2006. End of Life Options for Composite Waste Recycle , Reuse or Dispose ? National Composites Network Best Practice Guide, National Composites Network. Cambridge.

Hickle, G.T., 2014. Moving beyond the “patchwork:” a review of strategies to promote consistency for extended producer responsibility policy in the U.S. J. Clean. Prod. 64, 266–276. https://doi.org/10.1016/j.jclepro.2013.08.013

Job, S., 2014. Recycling composites commercially. Reinf. Plast. 58, 32–38. https://doi.org/10.1016/S0034-3617(14)70213-9

Job, S., Leeke, G., Mativenga, P.T., Oniveux, G., Pickering, S., Shuaib, N.A., 2016. Composites Recycling: Where are we now? Herts.

Khor, K., Udin, Z., 2012. Impact of Reverse Logistics Product Disposition towards Business Performance in Malaysian E&E Companies. J. Supply Chain Cust. Relatsh. Manag. 2012, 1–19. https://doi.org/10.5171/2012.699469

Larsen, K., 2009. Recycling wind. Renew. Energy Focus 9, 70–73.

Lindhqvist, T., 2000. Extended Producer Responsibility in Cleaner Production: Policy Principle to promote Environmental Improvements of Product Systems. Soc. Sci. Lund University.

Mapline, 2016. Mapline [WWW Document]. Mapline. URL www.mapline.com (accessed 9.27.16).

Massarutto, A., 2014. The long and winding road to resource efficiency – An interdisciplinary perspective on extended producer responsibility. Resour. Conserv. Recycl. 85, 11–21. https://doi.org/10.1016/j.resconrec.2013.11.005

Mativenga, P.T., Agwa-Ejon, J., Mbohwa, C., Sultan, A.A.M., Shuaib, N.A., 2017a. Circular Economy Ownership Models: A view from South Africa Industry. Procedia Manuf. 8, 284–291. https://doi.org/10.1016/j.promfg.2017.02.036

Mativenga, P.T., Mohamed Sultan, A.A., Agwa-ejon, J., Mbohwa, C., 2017b. Composites in a Circular Economy : A study of United Kingdom and South Africa. Procedia CIRP 61,

27

691–696. https://doi.org/10.1016/j.procir.2016.11.270

McKerlie, K., Knight, N., Thorpe, B., 2006. Advancing Extended Producer Responsibility in Canada. J. Clean. Prod.

Naidu, K., Mokhlis, H., Bakar, A.H.A., 2014. Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control. Int. J. Electr. Power Energy Syst. 55, 657–667. https://doi.org/10.1016/j.ijepes.2013.10.022

Nawrocka, D., Parker, T., 2009. Finding the connection: environmental management systems and environmental performance. J. Clean. Prod. 17, 601–607. https://doi.org/10.1016/j.jclepro.2008.10.003

Newman, W.R., Sridharan, V., 1995. Linking manufacturing planning and control to the manufacturing\nenvironment. Integr. Manuf. Syst. 6, 36–42. https://doi.org/10.1108/09576069510088952

OECD, 2001. Extended Producer Responsibility A Guidance Manual For Governments.

Oliveux, G., Dandy, L., Leeke, G., 2015. Current status of recycling of fibre reinforced polymers: Review of technologies, reuse and resulting properties. Prog. Mater. Sci. 72, 61–99.

Özdemir-Akyıldırım, Ö., 2015. Deposit-refund system vs. compliance scheme membership: How to comply with producer responsibility regulations? Int. J. Prod. Econ. 162, 25–44. https://doi.org/10.1016/j.ijpe.2014.12.027

Preston, F., 2012. A Global Redesign? Shaping the Circular Economy. Energy, Environ. Resour. Gov. 1–20. https://doi.org/10.1080/0034676042000253936

Rao, V., 2007. Decision Making in the Manufacturing Environment, Springer. Springer-Verlag, Surat. https://doi.org/10.1007/978-1-84628-819-7

RenewableUK, 2013. Small and Medium Wind UK Market Report, RenewableUK’s Wind Energy. London.

Riedel, J.C.K.H., Pawar, K.S., 1997. The consideration of production aspects during product design stages. Integr. Manuf. Syst. 8, 208–214. https://doi.org/10.1108/09576069710182027

Sachs, N.M., 2006. Planning the Funeral at the Birth : Extended Producer Responsibility in the European Union and the United States. Harvard Environ. Law Rev. 30, 51–98.

Scheijgrond, J.-W., 2011. Extending producer responsibility up and down the supply chain, challenges and limitation. Waste Manag. Res. 29, 911–8. https://doi.org/10.1177/0734242X11416540

Shaharudin, M.R., Govindan, K., Zailani, S., Tan, K.C., 2015. Managing product returns to achieve supply chain sustainability: an exploratory study and research propositions. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2015.03.074

Shuaib, N.A., Mativenga, P.T., Kazie, J., Job, S., 2015. Resource efficiency and composite waste in UK supply chain. Procedia CIRP 29, 662–667. https://doi.org/10.1016/j.procir.2015.02.042

Singh, J., Ordoñez, I., 2015. Resource Recovery from Post-Consumer Waste: Important Lessons for the Upcoming Circular Economy. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2015.12.020

28

Spicer, A.J., Johnson, M.R., 2004. Third-party demanufacturing as a solution for extended producer responsibility. J. Clean. Prod. 12, 37–45.

Sultan, A.A., Lou, E., Mativenga, P.T., 2017. What should be recycled: An integrated model for product recycling desirability. J. Clean. Prod. 154, 51–60. https://doi.org/10.1016/j.jclepro.2017.03.201

Tan, P.-N., Steinbach, M., Kumar, V., 2006. Introduction to Data Mining, 2nd editio. ed. Pearson, Michigan.

Think Defence, 2012. UK Armed Forces Aircraft Numbers [WWW Document]. URL www.thinkdefence.co.uk

WRAP, 2015. WRAP and the circular economy. Waste Resour. Action Program.

Yang, Y., Boom, R., Irion, B., van Heerden, D.-J., Kuiper, P., de Wit, H., 2012. Recycling of composite materials. Chem. Eng. Process. Process Intensif. 51, 53–68. https://doi.org/10.1016/j.cep.2011.09.007

Zaki, M., Hasrulnizzam, W., Mahmood, W., Rizal, M., Nizam, M., Rahman, A., 2015. The implementation of cleaner production practices from Malaysian manufacturers ’ perspectives. J. Clean. Prod. 108. https://doi.org/10.1016/j.jclepro.2015.07.102

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