Recycling, production and planning
Being a strategic and a tactical issue to deal with for companies and governments, there are a significant number of studies focusing on the process and material analyses, product design, production and recycling planning.
Kirkpatrick et al. [
32] investigated the environmental impacts of ELV disposal in the UK. Bellman and Khare [
33] attempted to analyze financial sources of the ELV management and suggested a few policies about producer responsibility. Hartman et al. [
34] presented a project to develop and demonstrate a method and the business potential in re-using components from ELVs. Díaz and Fernández [
35] defined future treatment centers for ELV processing. Mark et al. [
36] presented a demanufacturing chain under different scenarios to do economic analyses for vehicle instrument panels. Johnson and Wang [
37] presented a demanufacturing optimization model to evaluate economics and material destinations within the requirements of the new ELV legislation. Petrov [
38] developed a concept for an ELV recycling system for a Russian automotive company.
Johnson and Wang [
39] presented an analysis tool that uses demanufacturing optimization to evaluate the economics and material destinations. Van Schaik et al. [
40] proposed a dynamic optimization model for recycling aluminum from passenger vehicles. Boon et al. [
41] proposed a model to assess the materials streams and process profitability for several clean vehicles via Goal programming. Petrov [
42] researched recyclability of all basic LADA family automobiles to meet European ecological requirements. Castro et al. [
43] performed the Life cycle impact assessment of the average passenger vehicles of the Netherlands, with emphasis on the current dismantling and recycling practices.
Gesing [
44] reviewed current and future recycling technologies with a focus on increasing light material content. Mark and Kamprath [
45] summarized various bonding applications and their materials aspect for vehicle lightweighting. Van Schaik and Reuter [
46] developed dynamic modeling and simulation approaches to illustrate the influence of various parameters on the recycling rate. Van Schaik et al. [
47] proposed a nonlinear optimization model to describe the relationship between particle size reduction and liberation during the shredding and recycling of ELVs. Kim et al. [
48] surveyed using some questionnaires processing rates and management status in Korea to aid the establishment of ELV management policies. Schmidt et al. [
49] aimed at identifying the environmental impacts and relevance for combinations of recycling/recovery and lightweight vehicle design options over the whole life cycle. Pelletiere and Reinert [
50] presented a database on used automobile protection, and employed gravity models of the used automobile trade. Bandivadekar et al. [
51] presented a simulation model for material flows and economic exchanges to examine the effect of future changes in vehicle material compositions on the US recycling infrastructure.
Sawyer-Beaulieu and Tam [
52] used Life-cycle assessment (LCA) to increase the understanding of and consequently improve the ELV management process in North America. Seo et al. [
53] published a study about the ELV management and ASR characterization in Korea. Choi et al. [
54] presented a mixed-integer programming model for tactical process planning in the case of traditional US automotive shredders. Castro et al. [
55] presented a simulation model that describes the relationships between product design and the liberation level attained by the shredding of passenger seats. Forslind [
56] analyzed the consequences of implementing EPR for vehicle recyclers in Sweden.
Chen [
57] conducted a study to address the sustainable recycling of Chinese automobile products within the period of 2006–2010. Ferrao et al. [
58] assessed the influence of the ELV Directive on the profitability of vehicle dismantlers and shredders. Krinke et al. [
59] compared the environmental profiles of two different ELV recycling methods. Reuter et al. [
60] explored the limits of ELV recycling. Forton et al. [
61] emphasized current issues and drivers at play on the ELV management in the UK to outline their actual effects on present practice. Finkbeiner et al. [
62] expressed the use of LCA on Mercedes-Benz S-Class vehicles. Mazzanti and Zoboli [
63] addressed the ways specific to economic instruments reflecting the producer responsibility principle in waste and recycling policy. Amaral et al. [
64] discussed how far recycling technology innovation can be a major driver for technology shift in the automobile industry. Ferrao and Amaral [
65] developed technical cost models to assess the economics of dismantling and shredding activities. Pelletiere and Reinert [
66] modeled the used automobile exports of Japan and the USA using two alternative gravity models.
Coates and Rahimifard [
67] provided an overview of the stakeholders and their relationships within the UK recovery chain and discussed the development of an ELV costing framework. Jeong et al. [
68] focused on the ELV treatment system in Korea using LCA methodology to evaluate its environmental performance and identify potential improvement opportunities. Joung et al. [
69] investigated the recycling rate and management status to aid the establishment of a policy for the ELV management in Korea. Mergias et al. [
70] used the PROMETHEE method to select the best compromise scheme for the ELV management in Cyprus. Dalmijn and De Jong [
71] analyzed the development of the vehicle recycling industry in the EU. Giannouli et al. [
72] developed a methodology and technical model for the evaluation of waste produced from road vehicles. Sakai et al. [
73] investigated the unintentional formation, decomposition, and emission-control performance of POPs during ASR incineration. Williams et al. [
74] proposed a MILP model for making tactical decisions regarding what extent to process and reprocess materials. Alonso et al. [
75] published a research project to contribute cost-effective and eco-efficient electrical and electronic systems components in the automotive industry. Frad and Revnic [
76] presented a method to assure the achievement of the required eco-efficiency rates, integrated into the software tool for car manufacturing. Ribeiro et al. [
77] modified a multi-material car component which is a part of the current automotive brake system, by its original manufacturer. Fuse et al. [
78] quantified the outflow of base metals indirectly exported from Japan in the form of ELVs.
Ignatenko et al. [
79] extended the optimization model proposed by Reuter et al. [
60] to add thermal treatment processes and energy recovery constraints. Qi and Hongcheng [
80] proposed a MILP model for designing an ELV recovery network constituted from dismantling centers and processing facilities. Sawyer-Beaulieu and Tam [
81] used LCA to analyze ELV dismantling and shredding processes. Smith and Keoleian [
82] investigated the energy savings and pollution prevention in the US through remanufacturing a midsized automotive gasoline engine. Fuse and Kashima [
83] developed an automobile recycling input–output analysis-based evaluation method to examine the appropriateness of the recycling scheme for ELVs imported from Japan. Qu and Williams [
84] formulated automotive reverse production planning and pricing problems in a nonlinear programming model to develop an approximate supply function for hulks.
Chondros [
85] reviewed ELV treatment alternatives to ease the creation of an efficient ELV management system in different local conditions. Puri et al. [
86] stimulated material alternatives and end-of-life strategies for automotive components. Amelia et al. [
87] identified the existing conditions of automotive reuse in Malaysia by conducting interviews in selected local automotive and component manufacturers. Chen and Zhang [
88] provided insight into current thinking within China about the ELV management as well as vehicle recovery activities. Kumar and Sutherland [
89] focused on certain profit-enhancement strategies that may be employed to ensure the economic sustainability of ELVs. Fuse et al. [
90] proposed an estimation method for calculating the number of used passenger cars employed in world trade. Differently, Fuse et al. [
91] used the regression analysis to estimate the global flow of base metals (iron, aluminum, copper, lead, and zinc) in the used automobile trade.
Santini et al. [
92] studied the impact that pre-shredder treatment could have on achieving 85% recyclability rate in 2015. Chen et al. [
93] thoroughly described the principles and characteristics of the vehicle recycling system in Taiwan. Go et al. [
94] provided a framework for automotive components to be designed for ease of recovery by optimizing the disassembly sequence. Mathieux and Brissaud [
95] presented a new method to elaborate end-of-life product-specific material flow analysis, based on data obtained from statistics as well as from expert elicitation.
Agbo [
96] quantified the available salvage value and service materials potential from imported used vehicles in Nigeria. Duranceau and Sawyer-Beaulieu [
97] determined and quantified today’s actual ELV disposition rates based on their age and material content. Hedayati and Subic [
98] proposed a decision-making support framework for the recovery of ELVs to provide an integrated sustainable treatment option. Kibira and Jain [
99] studied the impact of hybrid and electric vehicles on the profitability of the recycling infrastructure. Santini et al. [
100] reported a shredder campaign trial developed and performed in Italy at the beginning of 2008. Xi et al. [
101] proposed a new method for predicting the residual strength and life of reused components or parts of ELVs. Zoraga et al. [
102] calculated energy consumption and carbon dioxide emissions of ELV recycling. Che et al. [
103] presented the ELV recycling system of Japan, China and Korea and in developing countries as well. Nazmi et al. [
104] suggested an ANN-based tool for predicting the critical stress life of a vehicle door with focusing on the optimal reusability.
Filho [
105] analyzed the various constituent vehicle materials and their impact on the environment in Brazil. Fiore et al. [
106] presented a characterization and valorization study about ASR in Italy. Millet et al. [
107] proposed a method based on an impact module on recycling rate indicators for identifying the worst recycling case. Nakamura et al. [
108] presented a novel approach to quantifying quality and dilution losses through hybrid input–output analysis. Santini et al. [
109] investigated ASR pre-treatment and pyrolysis to determine whether the ELV recycling target could be achieved by car fluff mechanical separation. Cheng et al. [
110] introduced a preliminary approach to examine the operational characteristics of the ELV recycling business in Taiwan. Hatayama et al. [
111] discussed how the recycling of aluminum will change until 2050, focusing on the introduction of next-generation vehicles and scrap sorting technology. Wang and Chen [
112] analyzed the current ELV recycling system in China and introduced an automotive product recycling technology roadmap. Simic and Dimitrijevic [
113] expanded the linear programming modeling framework proposed by Simic and Dimitrijevic [
114] to incorporate the vehicle hulk selection problem. Simic and Dimitrijevic [
114] presented a tactical production planning problem for vehicle recycling factories in the EU legislative and global business environments.
Arena et al. [
115] developed a performance measurement system to help automotive manufacturers to assess their technological options for sustainable mobility. Simic and Dimitrijevic [
116] proposed a short-term ASR recycling planning model for the Japanese vehicle recycling industry. Simic and Dimitrijevic [
117] developed a risk explicit MINP model for optimal long-term planning in the EU vehicle recycling facilities. Berzi et al. [
118] built a process simulation model that could be used for layout planning of ELV dismantling facilities. Tasala Gradin et al. [
119] applied the LCA method to compare two waste management scenarios: manual disassembly and shredding. Saavedra et al. [
120] presented an exploratory study on the current remanufacturing scenario and its main characteristics within the Brazilian automotive sector. Schmid et al. [
121] present the results from the quantitative and qualitative characterization of different material flows resulting from the three experimental campaigns on an industrial site. Hu and Kurasaka [
122] developed a projection model for ELV distribution per population at the provincial level in China.
Miller et al. [
123] examined the challenges of plastics recycling in the North American automotive industry. Ruffino et al. [
124] performed an economic assessment of a hypothetical industrial recovery process of light ASR, obtained by transferring the results gathered at lab scale to full scale. Sawyer-Beaulieu et al. [
125] presented strategies and actions for decreasing the lifecycle impact of automobiles. Tian and Chen [
126] illustrate the difficulty of handling polymers from a vehicle dashboard. Ahmed et al. [
127] examined the current state of the ELV management in Malaysia. Lu et al. [
128] identified drivers for new joining solutions in the automotive industry and specifically reviewed the current use of adhesive technology in ELVs. Yano et al. [
129] investigated the dynamic substance flow of lead as a representative toxic substance in ELVs and ASR. They applied a population balance model for estimating the number of generated ELVs in Japan between fiscal years 1990–2020.
El Halabi et al. [
130] assessed the environmental impact of using a multi-dismantling machine for material separation. Chen et al. [
131] applied dynamic modeling and cost–benefit analysis to investigate how policies may affect the recycling of ELVs in China. Despeisse et al. [
132] proposed policy, technical and business recommendations to improve reuse, recycling, and recovery rates. Ohno et al. [
133] used a waste input–output material flow analysis to investigate the content of alloying elements in ELVs. Sawyer-Beaulieu and Tam [
134] discussed the challenges anticipated with the development of ELV management systems. Yi and Park [
135] developed a smart dismantling monitoring and smart trolley system for an ELV recycling center. Simic and Dimitrijevic [
136] formulated and comprehensively tested a model for optimal long-term planning of vehicle recycling in the Republic of Serbia. The lifespan of a vehicle determines the amount of end-of-life flows. Oguchi and Fuse [
137] proposed a straightforward method for estimating the lifespan distribution of passenger cars based on the age profile of in-use cars.
Belboom et al. [
138] undertook an environmental evaluation of hybrid vehicles recycling using industrial data from Comet Traitement SA in Belgium. Desnica et al. [
139] presented an AHP approach to select equipment for detoxification of ELVs. Inghels et al. [
140] assessed the influence of material composition, amount and lifespan of passenger cars on the ELV management in Belgium. Junior et al. [
141] addressed vehicle recycling processes and manufacturer responsibility around the globe and the benefits to the economy, society, and environment. Pan and Li [
142] employed an improved emergy analysis with traditional and revised emergy indices for evaluation of the efficiency and sustainability of ELV recycling enterprises. Ahmed et al. [
143,
144] used DEMATEL and extent analysis method on the fuzzy AHP to rank ELV management alternatives concerning several sustainable criteria. Pourjavad and Mayorga [
145] also proposed an integrated fuzzy DM framework to evaluate sustainable ELV strategies. Pourjavad and Mayorga [
146] coupled the fuzzy AHP and fuzzy TOPSIS methods to rank seven ELV management strategies. Raja Mamat et al. [
147] develop a framework for the ELV management in Malaysia. Li et al. [
148] evaluated the environmental impacts of ELV recycling processes in China. Tian and Chen [
149] used the fuzzy AHP technique and cost–benefit analysis to compare five manual dismantling scenarios in China. Xia et al. [
150] applied cost–benefit analysis to perform the construction and investment analysis of an ELV disassembly plant in China. Zhou et al. [
151] developed a multi-criteria model based on the fuzzy VIKOR technique to evaluate ELV recycling service providers from the perspective of sustainability. Diener and Tillman [
152] examined the case of an automotive component manufacturer to investigate its ELV management. Xu et al. [
153] conducted a scenario analysis to determine the amount of rare earth elements that can be recovered from ELVs in Japan based on a dismantling survey, chemical identification, and substance flow analysis. Yano et al. [
154] used a population balance model for estimating the number of end-of-life hybrid electric vehicles generated in Japan during fiscal years 2010–2030. Besides, the amounts of rare earth elements contained in a hybrid transmission and a NiMH battery unit were presented.
Andersson et al. [
155] utilized the technological innovation system framework to identify key functions from 1910 to 2010 that enabled ELV iron recycling in Sweden. Ene and Öztürk [
156] developed an approach for predicting the number of ELVs that will be generated in the future. Gan and Luo [
157] presented a fuzzy-based DEMATEL approach to identify critical factors influencing the recycling rate of ELVs. Karaeen et al. [
158] presented a concept for the second life cycle of vehicles. Soo et al. [
159] discussed a comparative study on the environmental performance of the current ELV recycling processes between Australia and Belgium. Soo et al. [
160] analyzed the joining technologies used in the automotive industry to identify the ELV recyclability. Nakano and Shibahara [
161] used the LCA method for quantifying the amounts of greenhouse gases emitted when recycling ELVs by using the traditional shredding approach and the whole recycling approach, in which ELVs are pressed and transferred to an electric furnace or converter. Endo and Fuse [
162] explored and reduced the uncertainty in international trade for used automobiles and engines by correcting outliers and missing values.
Khodier et al. [
163] focused on challenges around ASR processing and disposal in the UK. Zhang and Chen [
164] used the AHP method to compare four ELV dismantling planning scenarios. Hao et al. [
165] aimed to better manage the reverse supply chain of the automotive industry in the context of green, circular, and sustainable development. Mohan and Amit [
166] proposed a system dynamics model to analyze informal dismantling facilities in India, which operate like a perfectly competitive market. Raja Mamat et al. [
5] proposed a performance evaluation tool based on the Analytic Hierarchy Process for implementation, monitoring and continuous improvement of the Malaysian ELV management system. Rosa and Terzi [
167] evaluated the current economic performances of the Italian ELV recovery chain using the system dynamics simulation approach. Zhang and Chen [
168] constructed an Arena-based simulation tool to analyze four scenarios of an ELV disassembly line in China. Wong et al. [
169] proposed a new concept of a processing framework to utilize ELV waste to construction industries via a new trend of circular economy applications in Malaysia. Ortego et al. [
170] proposed a downcycling assessment methodology based on the thermodynamic rarity indicator for accounting quantity and quality of the materials lost in the ELV recycling process. Lin et al. [
171] used a population balance model for predicting the number of generated ELVs in Kinmen, Taiwan, in the period 1960–2050. They presented material flow and economic analyses of a dismantling business in small islands. Xu et al. [
172] performed a scenario analysis to determine the amount of five precious metals that could be returned to material streams from ELVs based on the dismantling survey, chemical identification, and substance flow analysis. Also, a population balance model was utilized for forecasting the number of generated ELVs in Japan in the period 2015–2040.
Miskolczi et al. [
173] proposed a study about the modification of zeolite catalysts by metal loading for using ELV plastic waste pyrolysis. Sato et al. [
174] proposed an evaluation method to assess benefits with enabling energy consumption and carbon dioxide emission. Arora et al. [
175] attempted to use the shared responsibility based framework to explore and develop a business model of the ELV management in India. Mohamad-Ali et al. [
176] carried out a survey to identify the issues and factors of the ELV recovery system in Malaysia. Qiao et al. [
177] presented a survey that focused on the economic and environmental benefits of electric vehicle recycling in China. Wang et al. [
178] analyzed the efficiency of the ELV reverse logistics industry to improve resource utilization efficiency in Shanghai, China. Yang et al. [
179] presented a systematic index system in selecting criteria for sustainable ELV management via constructing a group DM approach in a fuzzy environment. Yano et al. [
180] conducted a dismantling survey and chemical analysis of six ELVs to estimate the content of valuable and toxic elements/substances.
Used lead-acid battery recycling is a growing hazardous industry. The lead issues from automotive battery recycling are major sources of soil contamination and human health exposure. The management of this hazardous waste from the ELV recycling process was mainly neglected in the previous reviews (Table
1). Hoffmann and Wilson [
181] provided a brief characterization of the lead-acid battery recycling industry in the Philippines. Haefliger et al. [
182] investigated a mass lead intoxication that occurred as a result of unsafe informal automotive lead-acid battery recycling in Dakar, Senegal. Gottesfeld et al. [
183] assessed soil contamination inside and outside recycling plants operating with government approval to recycle used lead-acid batteries in seven African countries. Several studies investigated soil contamination and human health exposure in the battery recycling craft village, Dong Mai, Vietnam [
184‐
188]. For instance, Ericson et al. [
184] evaluated the efficiency of a novel soil lead mitigation project, Noguchi et al. [
185], Daniell et al. [
186], and Eguchi et al. [
187] assessed human lead exposure, while Fujimori et al. [
188] studied the lead contamination level in surface soil on roads. Ericson et al. [
189] estimated the number of informal lead-acid battery recyclers and the number of exposed people in 90 low- and middle-income countries.
The previous studies, related to recycling processes and analyses of materials, mostly suggested solutions for various local problems. More general and global approaches are highly needed. Moreover, material concepts and perceptions of the vehicles tend to change. Thus, more studies regarding this issue are needed in the future. Furthermore, most of the studies which are considering the managerial perspective are suggesting solutions about economic and/or material issues. There are not enough studies that include social criteria. The participation of the public is an important factor for the ELV management. Owners of ELVs need to be encouraged to withdraw their vehicles from the traffic. For this reason, social acceptance and social awareness are also important problems to deal with for more effective ELV management.
Although there are various types of product design and production planning studies about the ELV management, there are not enough studies comparing the designing and planning systems as before and after. Effects of recycling friendly product design and production planning could be monitored via customer feedbacks, financial analyses, etc.
Due to new ELV regulations, producers’ responsibilities are gaining importance. Manufacturers are expected to make their designs and revise their production plans according to legislation. Herewith, there are several types of approaches studied by the researchers.
Network design
The recycling process of ELVs includes its own supply chain management problem. There are numerous studies in the literature which are suggesting approaches to cope with supply chain issues of the ELV management. Ahn et al. [
190] created an optimization tool for solving facility location problems and developed a simulation tool for ELVs of the German automobile industry. Schultmann et al. [
191] presented the peculiarities of establishing a closed-loop supply chain (CLSC) for ELVs. Mansour and Zarei [
192] developed a multi-period reverse logistics optimization model to locate ELV collection centers and vehicle dismantlers. Cruz-Rivera and Ertel [
193] constructed an uncapacitated facility location model to design a collection network for ELVs in Mexico.
Merkisz-Guranowska [
194,
195] formulated MILP models to determine the optimum locations of the key participants of the ELV recycling network. Zarei et al. [
196] designed a reverse logistics network for the management of the ELV recovery process. Harraz and Galal [
197] presented a mixed-integer lexicographic goal programming for designing a sustainable recovery network for ELVs in Egypt. Mahmoudzadeh et al. [
198] proposed a capacitated location-allocation model for determining locations of ELV collection points from the perspective of the third-party reverse logistics provider. Vidovic et al. [
199] presented a modeling approach that could be used to locate collection points for ELVs.
Merkisz-Guranowska [
200,
201] formulated a bi-objective mixed-integer linear programming model aiming at the reorganization and construction of the ELV recycling network in Poland. Farel et al. [
202] used a MILP modeling technique to determine the optimal topology and material flow in future ELV glazing recycling network. Gołebiewski et al. [
203] proposed a simulation approach that could be used to determine optimum locations for ELV dismantlers. Mahmoudzadeh et al. [
204] used a MILP formulation to solve a location-allocation problem of ELVs scrap yards in Iran.
Ene and Öztürk [
205] developed a model for managing reverse flows of ELVs within the framework of a multi-period, multi-stage, capacity-constrained network design problem. Simic [
206] developed a two-stage interval-stochastic programming model for supporting the management of ELV allocation under uncertainty. Simic [
207] proposed a fuzzy risk explicit MINP model for ELV recycling planning in the EU. Subulan et al. [
208] formulated a multi-objective, multi-echelon and multi-product mixed-integer linear programming model with fuzzy objectives for optimizing the lead-acid battery CLSC in Turkey.
Alsaadi and Franchetti [
209] studied on finding the optimum location for a processing facility for ELVs. Demirel et al. [
210] proposed a MILP model for reverse logistics network design including different actors taking part in the ELV recycling system. Simic [
211] presented a multi-stage interval-stochastic programming model for planning ELV allocation. Simic [
212] proposed an interval-parameter two-stage stochastic full-infinite programming model for ELV allocation management under multiple uncertainties. Simic [
4] developed an interval-parameter chance-constraint programming model for uncertainty-based decision-making in the ELV recycling industry under rigorous environmental regulations.
Phuc et al. [
213] formulated a fuzzy MILP model for designing a multi-echelon, multi-product reverse logistics network. Özceylan et al. [
214] presented a case study from Turkey based on CLSC for ELV treatment. Deng et al. [
215] established a simulation–optimization model for the location, path and inventory problem of ELV recycling systems. Lin et al. [
216] proposed a MILP model for the facility location-allocation problem of an ELV recovery network. Shankar et al. [
217] formulated a MILP model for the CLSC network with a multi-echelon inventory, multi-period planning, and multi-product scenario. Sun et al. [
218] developed a mixed-integer bilevel linear programming model to locate distribution centers for collecting ELV parts. The outer and inner optimization tasks were minimizing location costs and transportation costs, respectively. Ma and Li [
219] proposed a two-stage stochastic programming model for solving the lead-acid battery CLSC problem with random demands and returns.
Kuşakcı et al. [
220] modeled the problem of designing the ELV reverse logistics network for the Istanbul Metropolitan area as a fuzzy mixed-integer linear program. Xiao et al. [
221] developed a MILP model for constructing a four-tier reverse logistics network model, which included ELV sources, collection centers, remanufacturing centers, and dismantlers.
In addition, the publications including mathematical models were categorized based on the type of decision variables, optimization model and solution approach. They are summarized in Table
3.
Table 3Summary of the publications with an optimization model
2002 | | | ✓ | | ✓ | | | | ✓ | | ✓ | | ✓ | ✓ | | ✓ | | ✓ | | |
2003 | | | ✓ | | ✓ | | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2004 | | | ✓ | | | ✓ | | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2005 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | | ✓ | |
2005 | | | ✓ | | | | ✓ | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2006 | | | ✓ | | ✓ | | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2006 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | | ✓ | |
2007 | | | ✓ | | | | ✓ | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2008 | | | ✓ | | | | ✓ | | | ✓ | ✓ | ✓ | ✓ | | | | ✓ | ✓ | | |
2008 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | | ✓ | | ✓ | |
2008 | | | | ✓ | | ✓ | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2008 | | | ✓ | | | | ✓ | | ✓ | | | ✓ | ✓ | | | | ✓ | ✓ | | |
2009 | Cruz-Rivera and Ertel [ 193] | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | | ✓ | ✓ | | |
2010 | | ✓ | | | | | | ✓ | ✓ | | | ✓ | ✓ | | | | ✓ | | ✓ | |
2010 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2011 | | ✓ | | | | | ✓ | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2011 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2011 | Mahmoudzadeh et al. [ 198] | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2011 | | ✓ | | | | | ✓ | | | ✓ | ✓ | ✓ | ✓ | | | ✓ | | ✓ | | |
2012 | | ✓ | | | ✓ | | | | | ✓ | ✓ | ✓ | ✓ | | | ✓ | | ✓ | | |
2012b | Simic and Dimitrijevic [ 113] | | | ✓ | ✓ | | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2013a | | ✓ | | | | | ✓ | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2013a | Simic and Dimitrijevic [ 115] | | | ✓ | ✓ | | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2013 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | ✓ | | | ✓ | |
2013 | | ✓ | | | | | ✓ | | | ✓ | ✓ | ✓ | ✓ | | | ✓ | | ✓ | | |
2013b | Simic and Dimitrijevic [ 116] | | | ✓ | ✓ | | | | | ✓ | ✓ | ✓ | ✓ | | | ✓ | | ✓ | | |
2013 | Mahmoudzadeh et al. [ 204] | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | | ✓ | ✓ | | |
2015a | | ✓ | | | ✓ | | | | | ✓ | ✓ | ✓ | ✓ | ✓ | | ✓ | | ✓ | | |
2015b | | ✓ | | | ✓ | | | | | ✓ | ✓ | ✓ | ✓ | | ✓ | ✓ | | ✓ | | |
2015 | Simic and Dimitrijevic [ 135] | | | ✓ | ✓ | | | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2015 | | ✓ | | | | | ✓ | | ✓ | | ✓ | | ✓ | | | ✓ | | ✓ | | |
2015 | | | ✓ | | | | ✓ | | | ✓ | ✓ | ✓ | ✓ | | ✓ | | ✓ | ✓ | | |
2016a | | ✓ | | | ✓ | | | | ✓ | | ✓ | | ✓ | ✓ | | ✓ | | ✓ | | |
2016b | | ✓ | | | ✓ | | | | ✓ | | ✓ | | ✓ | ✓ | | ✓ | | ✓ | | |
2016c | | ✓ | | | | | ✓ | | ✓ | | ✓ | | ✓ | ✓ | | ✓ | | ✓ | | |
2016 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2016 | Alsaadi and Franchetti [ 209] | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2017 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | ✓ | | ✓ | ✓ | | |
2017 | | ✓ | | | ✓ | | | | ✓ | | ✓ | | ✓ | | | | ✓ | ✓ | | |
2018 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | | | ✓ |
2018 | | ✓ | | | ✓ | | | | ✓ | | ✓ | | ✓ | | | | ✓ | ✓ | | |
2018 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2018 | | ✓ | | | | | ✓ | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2018 | | | ✓ | | | | ✓ | | ✓ | | ✓ | | ✓ | ✓ | | | ✓ | ✓ | | ✓ |
2019 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | | ✓ | | ✓ | | |
2019 | | ✓ | | | ✓ | | | | ✓ | | | ✓ | ✓ | | ✓ | ✓ | | ✓ | | |
Available investigations that are suggesting approaches to cope with supply chain issues of the ELV management are mostly performed with deterministic data. ELV management systems are complex waste management systems with many uncertain components. Uncertainties also exist with economic and technical parameters, ELV supply, etc. Moreover, most of the real-life applications involve highly complex uncertainty. Therefore, an extension of the available modeling frameworks to address uncertainties can provide a much more realistic representation of ELV management systems.