A Simulation Analysis of Economic and Environmental Factors in the

Design of an Electric Vehicle Battery Reverse Supply Chain

Melissa Venegas Vallejos

, Andrew Greasley

and Aristides Matopoulos

Aston University, Birmingham, U.K.

Cranfield University, Cranfield, U.K.

Keywords: Electric Vehicle Battery, Reverse Supply Chain, Discrete-Event Simulation.

Abstract: This article presents a study of a discrete-event simulation model of a UK reverse supply chain (RSC) for

electric vehicle batteries. The purpose of the study is to use the model to run a set of simulated scenarios to

explore how different operational strategies affect the RSC design configuration. The performance of the RSC

can be measured in terms of its economic impact (such as the value of material recovered and production

savings) and environmental impact (such as batteries recovered, remanufactured and repurposed, kg of

materials recovered and CO

emissions reduction). A key outcome of the study is that supply chain

participants found that although they were aware of individual processes within the RSC the insights of the

model covering the whole RSC and the metrics generated would enable them to make better informed RSC

design decisions.

1 INTRODUCTION

Environmental, legal, social and economic factors

have been encouraging manufacturing companies to

adopt greener and more sustainable supply chain

practices and are accounting for the end-of-life (EoL)

of products (Kazemi, Modak and Govindan, 2019).

Consequently, businesses are now looking at supply

chains more broadly and considering the reverse

flow, creating reverse supply chains. A reverse supply

chain (RSC) consists of all the parties and processes

involved in collecting products from a customer to

recover value or dispose of them (Guide Jr. and Van

Wassenhove, 2002).

The automotive industry is one of the industries

experiencing significant challenges in their reverse

supply chains in the coming years due to the rapid

growth of electric vehicle (EV) adoption. Global EV

sales are expected to increase steadily in the coming

years, from 3.1 million in 2020 to 14 million in 2025

(BloombergNEF, 2021). Electric vehicle batteries are

the most critical component of electric vehicles

because they account for a significant part of the

vehicle's cost and are highly relevant for EV

https://orcid.org/0000-0001-8238-6004

https://orcid.org/0000-0001-6413-3978

https://orcid.org/0000-0002-5083-0534

development and adoption. Since EV batteries

typically last between 8 to 10 years, the EOL supply

chain of this component needs to be prepared to

handle the increasing volumes of batteries that are

going to reach their end-of-life in the following

decades.

Electric vehicle batteries require unique

management when reaching their EOL for several

reasons. Firstly, the EV battery industry may face a

shortage or rise in the price of some of the critical raw

materials used in battery production (International

Energy Agency, 2018; Moores, 2018). Therefore,

recovering EV battery materials could help save costs

and preserve raw materials. Secondly, lithium-ion,

the most common EV battery type, uses metals such

as lithium, cobalt, nickel, and graphite that may harm

the environment and human health if not disposed of

properly (Winslow, Laux and Townsend, 2018;

International Energy Agency, 2019). Therefore, the

EoL management of batteries contributes to the

reduction of the EV carbon footprint. Thirdly, several

potential risks are associated with battery handling,

and it is necessary to follow careful procedures to

minimise the risks (Zeng, Li and Liu, 2015).

Vallejos, M., Greasley, A. and Matopoulos, A.

A Simulation Analysis of Economic and Environmental Factors in the Design of an Electric Vehicle Battery Reverse Supply Chain.

DOI: 10.5220/0012851500003758

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2024), pages 399-406

ISBN: 978-989-758-708-5; ISSN: 2184-2841

399

Therefore, assigning this work to professional OEMs

(Original Equipment Manufacturers) and third-party

logistics 3PL providers is essential. Lastly, under the

latest Regulation (EU) 2023/1542 of the European

Parliament and of the Council concerning batteries

and waste batteries that was released in July 2023

(European Commission, 2023), EV manufacturers

are responsible for the environmental impacts of the

batteries used in their vehicles right up until the end-

of-life cycle. The UK is one of the most influential

electric vehicle markets in Europe. The British

government is supporting the electrification of the

automotive sector in several ways.

The UK and the European Union (EU) have

agreed to extend their tariff-free trade in electric

vehicles, potentially saving car manufacturers and

consumers up to £4.3 billion in additional costs

(GOV.UK, 2023b). Moreover, the UK government

has been attracting investment in EV battery

gigafactories and EV manufacturing. Nissan is

investing £3 billion to develop EVS in Sunderland. At

the same time, BMW is investing £600 million to

build Mini EVs in Oxford (GOV.UK, 2023a).

Envision and Tata are investing £450 million and £4

billion in new gigafactories (AESC, 2023; GOV.UK,

2023c).

Despite all the important investments in EV and

EV battery manufacturing, the UK end-of-life electric

vehicle supply chain is at an early stage. The number

of EVs (Electric Vehicles) and EV batteries reaching

their end-of-life is still low, and several EV

manufacturers have not defined the structure of their

EoL reverse supply chains yet.

Several authors have addressed the topic of

reverse supply chain design by developing models.

Some interesting models were found in the literature

(see, for example, Jindal & Sangwan, 2014; Ghorbani

et al., 2014; Das & Dutta, 2015). However, most of

these papers suggest alternatives to improve the

efficiency of the processes rather than to achieve

supply chain sustainability. There is also a lack of

industry case studies; most of the papers found in the

literature present illustrative cases with created data.

Some practical simulation models were found in the

literature (see Jayant et al., 2014; Yanikara & Kuhl,

2015) but they are generally limited to a quantitative

analysis without a thoughtful understanding of the

industry context and other factors that influence

design decisions such as industry stage, suppliers’

resources and capabilities or legislations.

Furthermore, the models studied mainly include

manufacturers and recyclers in their reverse supply

chain models but do not consider other key

stakeholders such as remanufacturers, refurbishing

companies and second-life repurposing companies.

The relevance of building appropriate relationships

between them to build successful and sustainable

supply chains is also overlooked.

Modelling a future sustainable EoL reverse

supply chain poses a number of challenges. In the

case of the EV battery industry, its UK EoL reverse

supply chain is still in a developing stage, and no

defined supply chain is currently operating and so the

EoL process flows for EV batteries are not clearly

defined. The technology for recycling, recovery and

remanufacturing is still under development. The

service providers and companies that offer EoL

services are at the moment handling low volumes of

batteries, and markets for the recovered products and

materials are still being explored. Moreover, the

legislation around the EoL treatment of EV batteries

is subject to change. Also, current legislation is

mainly focused on recycling as opposed to alternative

options for batteries such as remanufacturing and

repurposing.

This research draws on the preliminary

information collected from managers and directors

from companies that have experience providing EoL

services to the automotive industry and have worked

or have run pilots with EV batteries. This study

presents a potential UK EoL supply chain for electric

vehicle batteries that includes a dealer service centre,

a specialised authorised treatment facility (ATF)

network across the UK, a remanufacturing company,

a repurposing company and a recycling company.

These selected companies are key players in the EoL

supply chain for EV batteries since they are

responsible for collecting the EV batteries from EV

users and offer different recovery alternatives to

extend the life of EV batteries, components, and

materials. All the companies involved in this research

are UK-based. Even though this study focuses on the

UK context, the methodology can be used to study

other contexts, and the model can be easily adapted.

The objectives of the study are the following:

• To model a UK EoL RSC for EV batteries that

can be used to represent future design

configurations.

• To run a set of simulated scenarios to explore

how different sustainability strategies affect the

RSC design configuration and assess the

economic impact (such as the value of material

recovered and production savings) and

environmental impact (such as batteries

recovered, remanufactured and repurposed, kg of

materials recovered and CO2 emissions

reduction).

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2 THE SIMULATION STUDY

The main stages in the simulation study are now

presented with results from a scenario that assesses

the effect on the RSC design of batteries destined for

recycling, remanufacturing and repurposing

operations.

2.1 Data Collection/Process Mapping

The initial data of the current process was collected

through semi-structured interviews and

questionnaires. While the future RSC for EV batteries

model was abstracted and refined using facilitated

modelling (Robinson et al., 2014) sessions with

managers and directors from a scrap car recycling

company that manages an important ATF network,

remanufacturing company, repurposing company and

a lithium-ion battery recycler. Meanwhile

questionnaires were used to collect specific data

about the process characteristics such as processing

times, processing sequences and workforce

schedules. The participants of the facilitated

interventions were:

• Client_AC: Environmental Planning Manager –

Automotive company

• Client_RG: Technology and Innovation

Manager – Recycling group

• Client_EC: Company Director – Engineering

company

• Client_CF: Head of Forecasting – Consultancy

Firm specialised in lithium-ion battery and

electric vehicle supply chain.

• Client_CF2: Battery specialist and Senior

Engineer from Circular Economy team –

Consultancy Firm specialised in circular

economy projects.

The main processes that have been mapped and

included in the UK EOL supply chain for electric

vehicle batteries of this study are the following

(Figure 1):

• Batteries still under the warranty period are

collected by the dealer service centres, otherwise

batteries are collected through the ATFs.

• After the batteries are removed from the EVs,

they are sent to the Testing Facility where the

battery packs pass through an initial testing.

• Then, the batteries are disassembled to module

level, and tested to decide the EOL route.

• The modules in good condition are sent to the

remanufacturing plant for remanufacturing.

After the remanufacturing process is completed

the new batteries are tested and packed.

• The modules that did not pass the module testing

are disassembled to cell level.

• The battery cells then pass through a grading

process to measure their performance.

• The cells in good conditions that can be used to

build up new second-life batteries are sent to the

repurposing plant to be assembled, tested and

packed.

• The cells that did not pass the grading are sent to

a recycling plant where they are scrapped with

any valuable material recovered.

Figure 1: EOL EV batteries process map.

A Simulation Analysis of Economic and Environmental Factors in the Design of an Electric Vehicle Battery Reverse Supply Chain

401

2.2 Modelling Input Data

The demand level for battery processing has been

estimated based on secondary data from the

Department for Transport in the UK (Department of

Transport, 2022) and from an 8-10 years estimated

lifetime of EV battery (Gruber et al., 2011). The

processing times were estimated based on

information provided by the industry participants in

the study based in the recycling, remanufacturing and

repurposing sector.

2.3 Building the Model

The discrete-event simulation model of the EOL RSC

for EV batteries was built using the Arena Simulation

Software v16.2 (Rockwell Automation, 2023). The

software allowed the building up of a simplified RSC

network following the process map in Figure 1.

2.4 Validation

In this case study as the simulation model is

representing a potential EOL RSC that does not exist,

the validation was supported using the facilitated

modelling intervention sessions (Robinson et al.,

2014) with industry experts. The three aspects of

validation proposed by Pegden, Shannon and

Sadowski (1995) are used: conceptual validity,

operational validity and believability.

2.4.1 Conceptual Validity

Conceptual validity ensures that the model built

represents a credible approximation to the real-world

system. To confirm the conceptual validity of this

simulation model and increase its credibility,

facilitated sessions were conducted with potential

users of the simulation model. Individual facilitated

sessions were arranged with potential users of the

simulation model (Client_AC, Client_RG,

Client_EC, Client_CF, Client_CF2) for the validation

stage. In these meetings the conceptual model,

simplification and assumptions of the EOL RSC for

EV batteries of this study were shared with the

participants. Some of the key elements of the

conceptual model were explained and discussed. The

participants shared new insights about the current

situation of the EOL RSC of EV batteries in UK and

mainland Europe.

Client_AC, Client_RG. Client_EC, Client_CF

and Client_CF2 made some observations suggesting

changes in the activities shown in the process map.

For instance, the activity “Battery pack testing” was

added to the process map since Client_RG suggested

that new technology has been developed that allow

testing before battery pack disassembling. Client_RG

stated that even though disassembling often takes

place in an ATF or a recycler it would be better to

leave that activity to technical experts in a specialised

testing facility. An additional dismantling process to

the cell block has been added to the model based on

Client CG and Client CF2 feedback. According to on

Client CG and Client CF2, the process of dismantling

to cell level for repurposing is different from

dismantling for recycling because the dismantling for

recycling can be destructive and as a consequence

take less time.

2.4.2 Operational Validity

The operational validity can be usually confirmed by

comparing the results obtained in the model with the

real-world performance (Greasley, 2023). In this case

study, as the simulation model represents a potential

EOL RSC that does not exist, the validation was

conducted by conducting a sensitivity analysis of the

simulation model subsystems. Banks et al. (2005)

suggest some alternatives to validate the DES model

behaviour for systems with no operational or limited

historical data. The alternatives suggested by Banks

et al. (2005) are parameter sensitivity test and

structural sensitivity test.

For this study the operational validity was

confirmed by performing a sensitivity analysis of the

process durations and adapting chance decisions

points. The sensitivity analysis was conducted to

identify if the simulation model built behaves as

expected and ensure that the input data used, and

model representation are appropriate for the study

needs.

2.4.3 Believability

The third aspect of validation is believability. The

believability consists of ensuring that the module

outputs are credible for the simulation users

(Greasley, 2023). To ensure believability, individual

interviews were arranged with managers and

directors from a car manufacturer and companies

involved on the EOL management of EV batteries.

The simulation project objectives, the capabilities of

the simulation model and assumptions were

explained to the participants. To support the

explanation and further discussion of the simulation

model the structured walkthrough and animation

inspection were used. For the structured walkthrough

the Arena model flowchart was shown to the

participants to ensure that the model was a close

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representation of a potential EOL RSC for EV

batteries. The animation of the simulation model

running in slow speed was also shared live with the

industry experts to ask for their feedback. The

animation also included some performance metrics

such as Labour Cost, utilisation of resources.

Client_AC, Client_RG, Client_EC, Client_CF and

Client_CF2 validated that the metrics Labour cost,

and capacity of the system were relevant metrics to

assess the performance of the model proposed.

Client_EC suggested that for its company future

projects they are planning to have different

companies operating under the same roof doing the

disassemble, SOH assessment, remanufacturing,

repurposing, recycling. Client_CF also suggested to

choose a specific battery chemistry to make a more

detailed estimation of the specific raw material

recovered through recycling.

In addition, some changes were made in the

processing times, and number of resources of

bottleneck processes during the interviews to show in

a visual way how the queues and performance metrics

changed accordingly. Some processing times were

validated while others updated according to the

feedback and justifications of Client_AC, Client_EC,

Client_RG, Client_CF. Finally, the simulation model

animation display was used to obtain insights about

performance metrics that the industry experts were

interested in knowing from the simulation model.

2.5 Experimentation

A selection of battery routing scenarios were built

based on the discussions that had taken place in the

facilitated modelling sessions. When participants

were asked about the potential routes that batteries

would follow, they mentioned that the proportion of

batteries sent for recycling, remanufacturing and

repurposing will depend on several factors such as

country legislations, battery technology/chemistry

innovation and aftermarket. Participants Client_CF

and Client_CF suggested experiments with extreme

scenarios that consider a minimum of 50% recycling.

From these discussions a group of scenarios was

derived that consider a variation in the proportion of

batteries routed for remanufacturing, repurposing and

recycling (table 1):

• Baseline: it is the initial baseline scenario. This

group of scenarios considers that 50% of the

batteries are sent for recycling 25% for

remanufacturing and 25% for repurposing.

• Type C1: this scenario considers that all the

batteries (100%) that enter the system go for

recycling, which was achieved by sending

collected batteries for disassembling to the cell

level and then sending all of them for recycling.

• Type C2: the second type C scenario considers

that 50% of the batteries are sent for recycling

and 50% for remanufacturing. This was achieved

by sending 50% of the collected batteries for an

initial disassembling and then sending them for

recycling. The remaining 50% of the batteries

were sent for initial disassembling, module

testing, and remanufacturing.

• Type C3: the last scenario considers that 50% of

the batteries are sent for recycling and 50% for

repurposing. In this scenario, 50% of the

collected batteries were sent for initial

disassembling and then for recycling. At the

same time, the remaining 50% of the batteries

were sent for disassembling, testing and

repurposing.

For each of the scenarios, the resources were balanced

across each stage of the reverse supply chain,

considering a maximum of 80% of utilisation.

The results show the impact of selecting different

EoL strategies. For instance, the results of Type C1

scenario that considers 100% of batteries going direct

to recycling gives £36,500k as an average profit on

sales of recycled material and an average reduction of

45,625 CO

emission (kg CO

-eq).

In Type C2 scenario the profits for recycled

material and CO

emissions reductions went down by

50%; however, the remanufactured batteries allowed

savings of £10,949k and a reduction of emissions of

43,247 (kg CO

-eq).

In the case of scenario Type C3, sending 50% of

the batteries for recycling and 50% for repurposing

generated a £109,450k savings due to repurposing

and a CO

emission reduction of 69,172 (kg CO

-eq).

Table 1: Type C scenario conditions.

Battery routing scenarios

EoL Route Baseline Type C1 Type C2 Type C3

Recycling 50% 100% 50% 50%

Remanufacturing 25% - 50% -

Repurposing 25% - - 50%

A Simulation Analysis of Economic and Environmental Factors in the Design of an Electric Vehicle Battery Reverse Supply Chain

403

Table 2: Scenarios - Economic and environmental impact.

Impact metrics (Avera

e) Baseline Type C1 Type C2 Type C3

Profit on sales of recycle

material (£k) 18,820 36,500 18,252 18,260

emission reduction (kg CO

-eqv) 22,823 45,625 22,815 22,825

Savings due to remanufacturing (£k) 5,447 - 10,949 -

emission reduction (kg CO

-eqv) 21,515 - 43,247 -

Savings due to repurposing (£k) 54,656 - - 109,450

emission reduction (kg CO

-eqv) 34,543 - - 69,172

Table 2 shows a summary of the economic and

environmental impact of each of the experiments.

3 DISCUSSION

This research proposes a simulation modelling

approach used in an industry case study that

complements previous RSC modelling that has

mostly used linear and non-linear modelling (see, for

example, Jindal & Sangwan, 2014; Ghorbani et al.,

2014; Das & Dutta, 2015, Jayant et al., 2014;

Yanikara & Kuhl, 2015).

As Simchi-Levi (2014) and Sodhi and Tang

(2014) suggest, the models that come from a real

industry context are more valid and generalisable in

practice. Therefore, this research studies RSC design

issues in a real and challenging industry context of a

developing industry of a high-technology complex

product, namely the EV battery industry.

As the study participants suggested, there is no

certainty about the proportion of batteries that would

follow the recycling, remanufacturing and

repurposing routes. The EoL routes for batteries and

the future volume of batteries will be highly

dependent on the UK battery legislation and market

conditions. If legislation promotes recycling then it is

likely that the percentage of batteries that follow the

recycling route would increase. Whereas if the

legislation would set up remanufacturing or

repurposing targets, the proportions of batteries

following such routes may increase. Moreover, any

further changes in the UK government's phasing out

of petrol car use, such as presented by the British

government in September 2023 (Reuters, 2023),

could affect the number of EVs entering the market

and the number of EoL EV batteries returning from

the market. In addition, the proportion of batteries

sent to remanufacturing and repurposing will depend

on the market available for such products. If the

technology and chemistry continue evolving at a fast

pace, by the time the batteries return from the market,

the OEMs may require different batteries. In the case

of repurposed batteries, the market for them is still in

its infancy.

The participants stated that they have been

studying and assessing the different EoL processes in

isolation but have not seen a model of the whole RSC

for EV batteries before. Having a visual flexible

model able to represent a future supply chain that

does not exists would allow them to assess a range of

potential RSC configurations that follow different

processes, routes and volumes was considered

important for the study participants. In this case the

model proved to be useful to assess the impact of

different RSC configurations that follow different

sustainability strategies in terms of throughput,

resources required, capacity (number of batteries

processes, tonnes of material recycled,

remanufactured batteries, repurposed batteries) and

sustainability impact of changes (economic savings,

impact). The study participants agreed that the

metrics shown in the simulation model study would

allow them to conduct more accurate cost-benefit

analysis to make well-informed RSC design

decisions.

This paper contributes to the RSC literature, but it

is not without limitations. Conducting a case study in

a particular industry limits the option to generalise the

results and findings. Future research could benefit

from including participants from different industries

to validate if the research methodology can be used in

different industry contexts and generalise the results.

Another characteristic of the UK EV battery

industry is that it has few EoL service providers and

competitors. Hence, despite engaging with key

stakeholders with industry experience and

management expertise, future research would benefit

from including more industry participants from the

EV battery industry. For future research, the number

of participants could be increased to gain insights

from other OEMs, remanufacturers, recyclers and

ATFs in the sector with different perspectives on the

problem under study.

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Furthermore, future research could improve the

simulation model and adapt the model to take into

consideration the number and location of

decentralised facilities and the corresponding

transport implications (i.e. transport time, cost and

impact).

4 CONCLUSION

This paper presents a discrete-event simulation tool

that practitioners may use to model a future reverse

supply chain that does not exist and has limited

historical data. Managers and practitioners can use

the model proposed to measure the impact of changes

in processes, routes and volumes in terms of

throughput, capacity (number of batteries processes,

tonnes of material recycled, remanufactured batteries,

repurposed batteries) and sustainability impact of

changes (economic savings, CO

impact). The

insights of the model and the valuable metrics in

terms of capacity planning and economic and

environmental metrics were considered valuable by

the industry experts who participated in this study to

assess what-if scenarios and make informed RSC

design decisions.

REFERENCES

AESC (2023). Our Plant. Available at:

https://aesc.co.uk/about/what-we-do/our-plant/.

Banks, J. et al. (2005). Discrete-Event System Simulation.

4th edn. Upper Saddle River, NJ: Prentice Hall.

BloombergNEF (2021). Electric Vehicle Outlook 2021.

Available at: https://about.bnef.com/electric-vehicle-

outlook/.

Das, D., Dutta, P. (2015). Design and analysis of a closed-

loop supply chain in presence of promotional offer,

International Journal of Production Research, 53(1),

141–165.

Department for Transport (2022). Transport and

environment statistics: October 2022. Available at:

https://www.gov.uk/government/statistics/transport-

and-environment-statistics-2022

European Commission (2023). ‘Regulation (EU)

2023/1542 of the European Parliament and of the

Council concerning batteries and waste batteries’,

European Commission, 2023(June), pp. 1–117.

Available at: http://data.europa.eu/eli/reg/2023/1542

/oj%0Ahttps://eur-lex.europa.eu/legal-content/EN/

TXT/?uri=CELEX%3A52020PC0798.

Ghorbani, M., Arabzad, S. M. and Tavakkoli-Moghaddam,

R. (2014). A multi-objective fuzzy goal programming

model for reverse supply chain design, International

Journal of Operational Research, 19(2), 141–153. doi:

10.1504/IJOR.2014.058947.

GOV.UK (2023a). Pathway for zero emission vehicle

transition by 2035 becomes law. Available at:

https://www.gov.uk/government/news/pathway-for-

zero-emission-vehicle-transition-by-2035-becomes-law.

GOV.UK (2023b). Tariffs on electric vehicles avoided as UK

and EU extend trade rules. Available at: https://

www.gov.uk/government/news/tariffs-on-electric-vehi

cles-avoided-as-uk-and-eu-extend-trade-rules.

GOV.UK (2023c). Tata Group to invest over £4 billion in UK

gigafactory creating thousands of jobs. Available at:

https://www.gov.uk/government/news/tata-group-to-in

vest-over-4-billion-in-uk-gigafactory-creating-thousand

s-of-jobs.

Greasley, A. (2023). Simulation Modelling: Concepts, Tools

and Practical Business Applications. 1st edn. London:

Routledge.

Gruber, P. W. et al. (2011). Global Lithium Availability A

Constraint for Electric Vehicles?, Journal of Industrial

Ecology, 15(5), 760–775. doi: 10.1111/j.1530-9290

.2011.00359.x.

Guide Jr., V. and Van Wassenhove, L. (2002). The Reverse

Supply Chain, Harvard business review, 80(2), 25–26.

International Energy Agency (2018). Global EV Outlook

2018. Available at: https://www.iea.org/reports/global-

ev-outlook-2018.

International Energy Agency (2019). Global EV Outlook

2019.

Jayant, A., Gupta, P. and Garg, S. (2014). Simulation

modelling and analysis of network design for closed-

loop supply chain: A case study of battery industry, in

Procedia Engineering. doi: 10.1016/j.proeng.2014.1

2.465.

Jindal, A. and Sangwan, K. S. (2014). Closed loop supply

chain network design and optimisation using fuzzy

mixed integer linear programming model, International

Journal of Production Research, 52(14), 4156–4173.

Kazemi, N., Modak, N. M. and Govindan, K. (2019). A

review of reverse logistics and closed loop supply chain

management studies published in IJPR: a bibliometric

and content analysis, International Journal of

Production Research, 57(15–16), 4937–4960. doi:

10.1080/00207543.2018.1471244.

Moores, S. (2018). Energy storage technologies: the supply

chain risks and opportunities. Oxford.

Pegden, D., Shannon, R. and Sadowski, R. (1995).

Introduction to simulation using Siman. Second edition.

Mc-Graw-Hill.

Reuters (2023). Britain delays ban on new petrol and diesel

cars to 2035, prime minister says. Available at:

https://www.reuters.com/world/uk/britain-delays-ban-

new-petrol-diesel-cars-2035-pm-sunak-2023-09-20/.

Robinson, S. et al. (2014). Facilitated modelling with

discrete-event simulation: Reality or myth?, European

Journal of Operational Research, 234(1), 231–240.

doi: 10.1016/j.ejor.2012.12.024.

Rockwell Automation (2020). Arena Simulation Software.

Available at: https://www.arenasimulation.com/

(Accessed: 1 July 2020).

A Simulation Analysis of Economic and Environmental Factors in the Design of an Electric Vehicle Battery Reverse Supply Chain

405

Simchi-Levi, D. (2014). OM research: From problem-

driven to data-driven research, Manufacturing and

Service Operations Management, 16(1), 2–10. doi:

10.1287/msom.2013.0471.

Sodhi, M. S. and Tang, C. S. (2014). Guiding the next

generation of doctoral students in operations

management, International Journal of Production

Economics, 150, 28–36. doi: 10.1016/j.ijpe.

2013.11.016.

Winslow, K. M., Laux, S. J. and Townsend, T. G. (2018).

A review on the growing concern and potential

management strategies of waste lithium-ion batteries,

Resources, Conservation and Recycling, 129(October

2017), 263–277. doi: 10.1016/j.resconrec.2017.11.001.

Yanikara, F. S. and Kuhl, M. E. (2015). A simulation

framework for the comparison of reverse logistic

network configurations, Proceedings - Winter

Simulation Conf. doi: 10.1017/CBO97811074153

24.004.

Zeng, X., Li, J. and Liu, L. (2015). Solving spent lithium-

ion battery problems in China: Opportunities and

challenges, Renewable and Sustainable Energy

Reviews. doi: 10.1016/j.rser.2015.08.014.

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