E-Waste Reverse Supply Chain: A Review and Future Perspectives
Abstract
:1. Introduction
2. Electronic Waste Issues
3. Reverse Supply Chain
3.1. Definition of Reverse Supply Chain
3.2. Reverse Supply Chain Processes
3.2.1. Product Acquisition
3.2.2. Reverse Logistics
3.2.3. Inspection and Disposition
3.2.4. Refurbish
3.2.5. Redistribution and Sales
3.3. Differences between Forward and Reverse Supply Chains
4. E-Waste Reverse Supply Chains
4.1. Factors of Implementation
4.2. Performance Evaluation and Decision Making
4.3. Forecasting Product Returns
4.4. Research on E-Waste Reverse Supply Chain Network Design Models
5. Research Gaps and Potential Research Directions
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- It is important to establish an integrated approach (combining field trips, literature review, and quantitative data analysis) to manage e-waste in developing countries (i.e., India, Pakistan, Vietnam, the Philippines) since informal recycling practices are common in these countries. Recycling methods normally use larger labour force and low-level technology so a significant number of e-waste components are heading to landfills.
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- There are some challenges in the implementation of e-waste management for developing countries. Firstly, setting up formal processing of e-waste requires significant investment at the initial stage so informal collectors are popular in these countries. Secondly, since the regulations of e-waste management in developed countries are extremely stringent, these countries export their waste to developing countries, such as India, where costs of management are lower and the rules are not as tough [24]. Hence, Extended Producer Responsibility (EPR) is considered as a useful practice for e-waste management. This practice can share the responsibility with companies, consumers, and smaller waste collectors and expand the reach of waste clean-up operations and creates a formal structure for a profitable and efficient e-waste management program. EPR can be implemented well in the long term only if effective monitoring of the collection process and roles of all the stakeholders are clearly defined in an integrated manner. This would be an interesting topic in the future.
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- Implementing proper e-waste management is not only a practical and technical issue but also political and financial aspects. In the future study, decision-makers should align proposed improvements with regional priorities and with a mechanism for monitoring and evaluate changes to a management system.
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- Evaluation of the existing e-waste practices is primary and it leads to prioritizing actions that start by regulating aspects of e-waste. Future work should include legislations of e-waste disposal and product imports.
- Research group 1: factors affected e-waste RSC implementation
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- In the category of factors affecting e-waste RSC implementation, although there are various factors to be considered, such as regulations, top management, environmental issues, financial concerns and etc., consumers’ behaviour is one of the most vital factors which contributes to the success of e-waste RSC implementation. Their return intention is a crucial variable for the prediction of the quantity of e-waste, and as a result, this will become important inputs for electronic firms in implementing the design of their RSC systems. Even though a few studies examined customers’ behaviour and residents’ awareness for returning e-waste issues, their research just focused on some particular products such as mobile phones or computers. Hence, further studies can enhance the behaviour of customers for a variety of electronic products, like household devices or IT and telecommunications equipment.
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- Most studies in this category try to improve the awareness of inhabitants about e-waste by the public media, educational programmes, etc. The roles of government and industry through incentive campaigns to encourage consumers to return their end-of-life products can be an interesting topic in the future.
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- There is a lack of studies focusing on e-waste RSC implementations in rural areas. The residents and local governments in the countryside, especially in developing countries have less awareness of e-waste recycling and environmental protection [46]. Hence, research on factors affected e-waste RSC implementations in rural areas can be conducted in future studies.
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- Extended producer responsibility (EPR) policy aims to manage and treat their end-of-life products. This plays an important role in e-waste RSC implementations at an industry perspective, which can be enhanced to the implementation at the national level. More practical research specifying on EPR implementation for e-waste would be potential future studies.
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- Risk assessment is also an important factor in RSC operation but it has been not examined by existing studies. In future research, risk assessment will be an interesting topic to explore.
- Research group 2: Performance evaluation and decision making
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- E-waste is normally recognized as a complex issue to make a proper decision. The complexity is influenced by various elements such as various sources of collection, the flow of returned products, the numbers of decision-makers. Hence, it requires an appropriate decision to improve the RSC performance which should be based on the collective decision. In this context, focusing on the vagueness of different decision-makers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) and more criteria to interpret their interaction on the RSC performance would be potential topics in future.
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- To comply with governmental regulations and create a competitive advantage, there is a need for electronics industries to collaborate with third-party reverse supply chain providers to effectively manage their returned products [67]. Although some existing studies conducted the evaluation and selection of third-party RSC providers, the limited number of studies focused on multiple attribute evaluation of electronics companies for RSC collaborations. Hence, more research on this issue by considering the vagueness of the decision-making process could be a promising study direction in the future.
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- Another potential topis could be conducting game theory to examine the strategic interactions in the decision-making process among different partners in RSC operations for e-waste.
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- Some recent research has considered three suitable dimensions such as social, economic and environmental aspects. It would be an interesting direction if future studies could integrate the technology dimension into the sustainable RSC system. The applications of radio-frequency identification, the internet of things can implement for inventory management, tracking the flows of materials, the management of recovery information could be a new study direction in this category.
- Research group 3: Forecasting product returns
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- Most existing studies estimate the number of product returns by examining different variables such as data sales, the lifespan of products, material composition, etc. However, other factors like customs and culture, regulations, demographics and consumer’s income might affect the prediction of product returns. There is no study analysing these factors for estimating returned products, which might create potential future research.
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- Previous research estimated the data of weight and e-waste composition based on the literature. However, these data might not accurate or vary over the years due to technological innovations. This might create some level of uncertainty. Further, other data such as lifespan, market share and value of unit which were normally collected and analysed by consumer survey, official government statistics or stakeholders. This also leads to fuzzy or uncertain data. Hence, in such a context, uncertainty analysis could be possible research in future implementation.
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- Although different forecasting techniques were applied for recycling or remanufacturing products, the study for forecasting returned products until disposition level is quite limited.
- Research group 4: e-waste RSC network designs
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- Network design is a strategic issue and has received great attention from researchers. Existing research mainly focused on transportation, fixed, operating, and disposal costs in the total cost of RSC. They ignore risk factors involved RSC operation which has a significant influence on RSC costs [104,127]. Risks can be seen at treatment centres and transportation activities due to a variety of dangerous materials contained in e-waste [128,129,130]. Therefore, integrating risk factors in an RSC network design model can be an interesting topic for future direction.
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- Regarding uncertain issues, most of the existing studies assumed that all parameters in the RSC system are deterministic or known in advanced. However, RSC operation is generally recognized as dynamic in nature with a high degree of uncertain parameters, such as return rate, processing cost, transportation cost, capacity and so on [8,94,131,132,133]. To handle uncertain parameters, stochastic approaches have been proposed [89,104,114,115,134]. Nevertheless, there are two main issues in applying a stochastic method. The first one is that, in some practical cases, there is insufficient historical data to be used for uncertain parameters, so it is difficult to acquire the exact random distributions of these parameters. The second reason is that in most of the existing studies on RSC under uncertainty environment, a stochastic approach is applied through different scenarios for modelling the uncertainty which might result in heavily computational burdens [135]. Therefore, to deal with these issues, the fuzzy approach has emerged as a potential option because it can be flexible to cope with imprecise information and various kinds of uncertainty simultaneously with high computational efficiency [136]. A few studies [137,138,139] used the fuzzy approach in the field of supply chain management. However, current studies on the e-waste RSC model with the fuzzy approach are still in their infancy.
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- Most articles in this group neglect to specify the source of e-waste which normally comes from three main sources: households, industries, government sectors. This factor should be cooperated in the design phase of RSC network to provide better policy management for e-waste in terms of economic and environmental aspects.
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- RSC consists of a set of activities including disassembly, repair and recycling to recover returned products. Selecting partners in the RSC network is crucial to achieving optimal outcomes. In this regard, partner selections can be addressed as a multi-criteria decision-making problem. This step can be implemented prior to considering a network design problem. There is a clear deficit of applying an integrated model (e.g., fuzzy AHP and MILP) which comprises the selection of RSC partners and network design.
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- The number of research which considers a specific product of e-waste RSC network modelling is still limited. Only a few studies examined some specific products such as refrigerators, conditioners [1,97], mobile phones, cameras [123]. More product-oriented research, such as IT products, computers, medical devices, etc., should be investigated in future studies.
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- To solve a large scale of problems, meta-heuristic or heuristic algorithms like Genetic Algorithms, Ant Colony Optimization and Tabu search can be applied for obtaining better computational performance.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Doan, L.T.T.; Amer, Y.; Lee, S.-H.; Phuc, P.N.K.; Dat, L.Q. A comprehensive reverse supply chain model using an interactive fuzzy approach-a case study on the vietnamese electronics industry. Appl. Math. Model. 2019, 76, 87–108. [Google Scholar] [CrossRef]
- Doan, L.T.T.; Amer, Y.; Lee, S.-H.; Phuc, P.N.K. Optimizing the total cost of an e-waste reverse supply chain considering transportation risk. Oper. Supply Chain Manag. Int. J. 2018, 11, 151–160. [Google Scholar] [CrossRef]
- Namias, J. The Future of Electronic Waste Recycling in the United States: Obstacles and Domestic Solutions; Columbia University: Broadway, NY, USA, 2013. [Google Scholar]
- Gurtu, A.; Searcy, C.; Jaber, M. An analysis of keywords used used in the literature on green supply chain management. Manag. Res. Rev. 2015, 38, 166–194. [Google Scholar] [CrossRef]
- Mokhtar, A.R.M.; Genovese, A.; Brint, A.; Kumar, N. Improving reverse supply chain performance: The role of supply chain leadership and governance mechanisms. J. Clean. Prod. 2019, 216, 42–55. [Google Scholar] [CrossRef]
- Shi, J.; Liu, Z.; Tang, L.; Xiong, J. Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Appl. Math. Model. 2017, 45, 14–30. [Google Scholar] [CrossRef]
- Islam, M.T.; Huda, N. Reverse logistics and closed-loop supply chain of waste electrical and electronic equipment (weee)/e-waste: A comprehensive literature review. Resour. Conserv. Recycl. 2018, 137, 48–75. [Google Scholar] [CrossRef]
- Pishvaee, M.S.; Razmi, J. Environmental supply chain network design using multi-objective fuzzy mathematical programming. Appl. Math. Model. 2012, 36, 3433–3446. [Google Scholar] [CrossRef]
- Kuik, S.S. Development of an Integrated Performance Evaluation Framework for Product Returns and Recovery Operations. Ph.D. Thesis, University of South Australia, Adelaide, Australia, 2013. [Google Scholar]
- Singh, N.; Li, J.; Zeng, X. Global responses for recycling waste crts in e-waste. Waste Manag. 2016, 57, 187–197. [Google Scholar] [CrossRef]
- Balde, C.P.; Forti, V.; Gray, V.; Kuehr, R.; Stegmann, P. The Global E-Waste Monitor 2017: Quantities, Flows and Resources; United Nations University, International Telecommunication Union, and International Solid Waste Association: Geneva, Switzerland, 2017. [Google Scholar]
- Dwivedy, M.; Mittal, R.K. Future trends in computer waste generation in india. Waste Manag. 2010, 30, 2265–2277. [Google Scholar] [CrossRef]
- Kumar, A.; Holuszko, M.; Espinosa, D.C.R. E-waste: An overview on generation, collection, legislation and recycling practices. Resour. Conserv. Recycl. 2017, 122, 32–42. [Google Scholar] [CrossRef]
- Widmer, R.; Oswald, H.; Sinha-Khetriwal, D.; Schnellmann, M.; Böni, H. Global perspectives on e-waste. Environ. Impact Assess. Rev. 2005, 25, 436–458. [Google Scholar] [CrossRef]
- Cucchiella, F.; D’Adamo, I.; Koh, S.L.; Rosa, P. Recycling of weees: An economic assessment of present and future e-waste streams. Renew. Sustain. Energy Rev. 2015, 51, 263–272. [Google Scholar] [CrossRef]
- Gaidajis, G.; Angelakoglou, K.; Aktsoglou, D. E-waste: Environmental problems and current management. J. Eng. Sci. Technol. Rev. 2010, 3, 193–199. [Google Scholar] [CrossRef]
- Alam, M.; Bahauddin, K.M. Electronic waste in Bangladesh: Evaluating the situation, legislation and policy and way forward with strategy and approach. Present Environ. Sustain. Dev. 2015, 9, 81–101. [Google Scholar] [CrossRef]
- Lundgren, K. The Global Impact of E-Waste: Addressing the Challenge; International Labour Office: Genève, Switzerland, 2012. [Google Scholar]
- Wäger, P.A.; Hischier, R. Life cycle assessment of post-consumer plastics production from waste electrical and electronic equipment (weee) treatment residues in a central European plastics recycling plant. Sci. Total Environ. 2015, 529, 158–167. [Google Scholar] [CrossRef] [PubMed]
- Gregory, J.R.; Kirchain, R.E. A comparison of North American electronics recycling systems. In Proceedings of the 2007 IEEE International Symposium on Electronics and the Environment, Orlando, FL, USA, 7–10 May 2007; IEEE: Piscataway, NJ, USA, 2007; pp. 227–232. [Google Scholar]
- Kahhat, R.; Kim, J.; Xu, M.; Allenby, B.; Williams, E.; Zhang, P. Exploring e-waste management systems in the united states. Resour. Conserv. Recycl. 2008, 52, 955–964. [Google Scholar] [CrossRef]
- Hung Lau, K.; Wang, Y. Reverse logistics in the electronic industry of china: A case study. Supply Chain Manag. Int. J. 2009, 14, 447–465. [Google Scholar] [CrossRef]
- Afroz, R.; Masud, M.M.; Akhtar, R.; Duasa, J.B. Survey and analysis of public knowledge, awareness and willingness to pay in Kuala Lumpur, Malaysia—A case study on household weee management. J. Clean. Prod. 2013, 52, 185–193. [Google Scholar] [CrossRef]
- Chaudhary, M.; Shalender, K.; Mishra, A.K. Issues and challenges in e-waste management in India: A gap between theory and practice. IUP J. Bus. Strategy 2018, 15, 54–63. [Google Scholar]
- Manomaivibool, P.; Vassanadumrongdee, S. Buying back household waste electrical and electronic equipment: Assessing Thailand’s proposed policy in light of past disposal behavior and future preferences. Resour. Conserv. Recycl. 2012, 68, 117–125. [Google Scholar] [CrossRef]
- Agrawal, S.; Singh, R.K.; Murtaza, Q. A literature review and perspectives in reverse logistics. Resour. Conserv. Recycl. 2015, 97, 76–92. [Google Scholar] [CrossRef]
- Van Wassenhove, L. The reverse supply chain. Harv. Bus. Rev. 2002, 80, 25–26. [Google Scholar]
- Gupta, S.M. Reverse Supply Chains Issues and Analysis; CRC Press: Boca Raton, FL, USA, 2013. [Google Scholar]
- Prahinski, C.; Kocabasoglu, C. Empirical research opportunities in reverse supply chains. Omega 2006, 34, 519–532. [Google Scholar] [CrossRef]
- Yin, W. Reverse supply chain management. Master Thesis, University of Gothenburg, Gothenburg, Sweden, 2011. [Google Scholar]
- Garg, K.; Kannan, D.; Diabat, A.; Jha, P.C. A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. J. Clean. Prod. 2015, 100, 297–314. [Google Scholar] [CrossRef]
- Blackburn, J.D.; Guide, V.D.R.; Souza, G.C.; Van Wassenhove, L.N. Reverse supply chains for commercial returns. Calif. Manag. Rev. 2004, 46, 6–22. [Google Scholar] [CrossRef]
- Rogers, D.S.; Tibben-Lembke, R.S. Going Backwards: Reverse Logistics Trends and Practices; Reverse Logistics Executive Council Pittsburgh: Pittsburgh, PA, USA, 1999; Volume 2. [Google Scholar]
- Tibben-Lembke, R.S.; Rogers, D.S. Differences between forward and reverse logistics in a retail environment. Supply Chain Manag. Int. J. 2002, 7, 271–282. [Google Scholar] [CrossRef]
- Marx-Gomez, J.; Rautenstrauch, C.; Nürnberger, A.; Kruse, R. Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing. Knowl. Based Syst. 2002, 15, 119–128. [Google Scholar] [CrossRef]
- Dat, L.Q.; Truc Linh, D.T.; Chou, S.-Y.; Yu, V.F. Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Syst. Appl. 2012, 39, 6380–6387. [Google Scholar] [CrossRef]
- Ravi, V.; Shankar, R.; Tiwari, M. Analyzing alternatives in reverse logistics for end-of-life computers: Anp and balanced scorecard approach. Comput. Ind. Eng. 2005, 48, 327–356. [Google Scholar] [CrossRef]
- Chiou, C.Y.; Chen, H.C.; Yu, C.T.; Yeh, C.Y. Consideration factors of reverse logistics implementation -a case study of taiwan’s electronics industry. Procedia Soc. Behav. Sci. 2012, 40, 375–381. [Google Scholar] [CrossRef]
- Guarnieri, P.; e Silva, L.C.; Levino, N.A. Analysis of electronic waste reverse logistics decisions using strategic options development analysis methodology: A Brazilian case. J. Clean. Prod. 2016, 133, 1105–1117. [Google Scholar] [CrossRef]
- Rahman, S.; Subramanian, N. Factors for implementing end-of-life computer recycling operations in reverse supply chains. Int. J. Prod. Econ. 2012, 140, 239–248. [Google Scholar] [CrossRef]
- Jafari, A.; Heydari, J.; Keramati, A. Factors affecting incentive dependency of residents to participate in e-waste recycling: A case study on adoption of e-waste reverse supply chain in Iran. Environ. Dev. Sustain. 2017, 19, 325–338. [Google Scholar] [CrossRef]
- Janse, B.; Schuur, P.; de Brito, M.P. A reverse logistics diagnostic tool: The case of the consumer electronics industry. Int. J. Adv. Manuf. Technol. 2010, 47, 495–513. [Google Scholar] [CrossRef]
- Dixit, S.; Badgaiyan, A.J. Towards improved understanding of reverse logistics – examining mediating role of return intention. Resour. Conserv. Recycl. 2016, 107, 115–128. [Google Scholar] [CrossRef]
- Pandebesie, E.S.; Indrihastuti, I.; Wilujeng, S.A.; Warmadewanthi, I. Factors influencing community participation in the management of household electronic waste in West Surabaya, Indonesia. Environ. Sci. Pollut. Res. 2019, 26, 27930–27939. [Google Scholar] [CrossRef]
- Nduneseokwu, C.; Qu, Y.; Appolloni, A. Factors influencing consumers’ intentions to participate in a formal e-waste collection system: A case study of Onitsha, Nigeria. Sustainability 2017, 9, 881. [Google Scholar] [CrossRef] [Green Version]
- Cao, J.; Lu, B.; Chen, Y.; Zhang, X.; Zhai, G.; Zhou, G.; Jiang, B.; Schnoor, J.L. Extended producer responsibility system in china improves e-waste recycling: Government policies, enterprise, and public awareness. Renew. Sustain. Energy Rev. 2016, 62, 882–894. [Google Scholar] [CrossRef]
- Thi Thu Nguyen, H.; Hung, R.-J.; Lee, C.-H.; Thi Thu Nguyen, H. Determinants of residents’ e-waste recycling behavioral intention: A case study from Vietnam. Sustainability 2019, 11, 164. [Google Scholar] [CrossRef] [Green Version]
- Keh, P.; Rodhain, F.; Meissonier, R.; Llorca, V. Financial performance, environmental compliance, and social outcomes: The three challenges of reverse logistics. Case Study IBM Montp. Supply Chain Forum Int. J. 2012, 13, 26–38. [Google Scholar] [CrossRef]
- Lin, C.-H.; Wen, L.; Tsai, Y.-M. Applying decision-making tools to national e-waste recycling policy: An example of analytic hierarchy process. Waste Manag. 2010, 30, 863–869. [Google Scholar] [CrossRef] [PubMed]
- Ravi, V. Evaluating overall quality of recycling of e-waste from end-of-life computers. J. Clean. Prod. 2012, 20, 145–151. [Google Scholar] [CrossRef]
- Bereketli, I.; Erol Genevois, M.; Esra Albayrak, Y.; Ozyol, M. Weee treatment strategies’ evaluation using fuzzy linmap method. Expert Syst. Appl. 2011, 38, 71–79. [Google Scholar] [CrossRef]
- Liu, J.; Zhong, H.; Wei, W. Composition and evaluation of waste electric and electronic equipment reverse logistics capability. In Proceedings of the 2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 7–10 December 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 1199–1203. [Google Scholar]
- Govindan, K.; Popiuc, M.N. Reverse supply chain coordination by revenue sharing contract: A case for the personal computers industry. Eur. J. Oper. Res. 2014, 233, 326–336. [Google Scholar] [CrossRef]
- Ponce-Cueto, E.; Manteca, J.Á.G.; Carrasco-Gallego, R. Reverse logistics for used portable batteries in Spain: An analytical proposal for collecting batteries. In Information Technologies in Environmental Engineering: New Trends and Challenges; Golinska, P., Fertsch, M., Marx-Gómez, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2011; pp. 593–604. [Google Scholar]
- Tsai, W.-H.; Hung, S.-J. Treatment and recycling system optimisation with activity-based costing in weee reverse logistics management: An environmental supply chain perspective. Int. J. Prod. Res. 2009, 47, 5391–5420. [Google Scholar] [CrossRef]
- Dhib, S.; Addouche, S.-A.; El Mhamdi, A.; Loukil, T. Performance Study for a Sustainable Strategy: Case of Electrical and Electronic Equipments Waste; Springer International Publishing: Cham, Switzerland, 2016; pp. 572–587. [Google Scholar]
- Aidonis, D.; Achillas, C.; Folinas, D.; Keramydas, C.; Tsolakis, N. Decision support model for evaluating alternative waste electrical and electronic equipment management schemes—A case study. Sustainability 2019, 11, 3364. [Google Scholar] [CrossRef] [Green Version]
- Duygan, M.; Meylan, G. Strategic management of weee in Switzerland—Combining material flow analysis with structural analysis. Resour. Conserv. Recycl. 2015, 103, 98–109. [Google Scholar] [CrossRef]
- Jayant, A.; Gupta, P.; Garg, S.; Khan, M. Topsis-ahp based approach for selection of reverse logistics service provider: A case study of mobile phone industry. Procedia Eng. 2014, 97, 2147–2156. [Google Scholar] [CrossRef] [Green Version]
- Sahu, A.K.; Narang, H.K.; Rajput, M.S. A grey-dematel approach for implicating e-waste management practice: Modeling in context of indian scenario. Grey Syst. Theory Appl. 2018, 8, 84–99. [Google Scholar] [CrossRef]
- Tran, H.P.; Schaubroeck, T.; Nguyen, D.Q.; Ha, V.H.; Huynh, T.H.; Dewulf, J. Material flow analysis for management of waste tvs from households in urban areas of Vietnam. Resour. Conserv. Recycl. 2018, 139, 78–89. [Google Scholar] [CrossRef]
- Bahers, J.-B.; Kim, J. Regional approach of waste electrical and electronic equipment (weee) management in france. Resour. Conserv. Recycl. 2018, 129, 45–55. [Google Scholar] [CrossRef]
- Agrawal, S.; Singh, R.K.; Murtaza, Q. Reverse supply chain issues in Indian electronics industry: A case study. J. Remanufacturing 2018, 8, 115–129. [Google Scholar] [CrossRef] [Green Version]
- Shokohyar, S.; Mansour, S. Simulation-based optimisation of a sustainable recovery network for waste from electrical and electronic equipment (weee). Int. J. Comput. Integr. Manuf. 2013, 26, 487–503. [Google Scholar] [CrossRef]
- De Meester, S.; Nachtergaele, P.; Debaveye, S.; Vos, P.; Dewulf, J. Using material flow analysis and life cycle assessment in decision support: A case study on weee valorization in belgium. Resour. Conserv. Recycl. 2019, 142, 1–9. [Google Scholar] [CrossRef]
- Isernia, R.; Passaro, R.; Quinto, I.; Thomas, A. The reverse supply chain of the e-waste management processes in a circular economy framework: Evidence from Italy. Sustainability 2019, 11, 2430. [Google Scholar] [CrossRef] [Green Version]
- Govindan, K.; Jha, P.; Agarwal, V.; Darbari, J.D. Environmental management partner selection for reverse supply chain collaboration: A sustainable approach. J. Environ. Manag. 2019, 236, 784–797. [Google Scholar] [CrossRef] [PubMed]
- Phuc, P.N.K.; Yu, V.F.; Chou, S.-Y. Optimizing the fuzzy closed-loop supply chain for electrical and electronic equipments. Int. J. Fuzzy Syst. 2013, 15, 9–21. [Google Scholar]
- Hanafi, J.; Kara, S.; Kaebernick, H. Generating Fuzzy Coloured Petri Net Forecasting Model to Predict the Return of Products. In Proceedings of the 2007 IEEE International Symposium on Electronics and the Environment, Orlando, FL, USA, 7–10 May 2007. [Google Scholar]
- Xiaofeng, X.; Tijun, F. Forecast for the Amount of Returned Products Based on Wave Function. In Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, Xi’an, China, 26–27 December 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 324–327. [Google Scholar]
- Araújo, M.G.; Magrini, A.; Mahler, C.F.; Bilitewski, B. A model for estimation of potential generation of waste electrical and electronic equipment in Brazil. Waste Manag. 2012, 32, 335–342. [Google Scholar] [CrossRef]
- Steubing, B.; Böni, H.; Schluep, M.; Silva, U.; Ludwig, C. Assessing computer waste generation in chile using material flow analysis. Waste Manag. 2010, 30, 473–482. [Google Scholar] [CrossRef]
- Andarani, P.; Goto, N. Potential e-waste generated from households in indonesia using material flow analysis. J. Mater. Cycles Waste Manag. 2014, 16, 306–320. [Google Scholar] [CrossRef]
- Petridis, N.E.; Stiakakis, E.; Petridis, K.; Dey, P. Estimation of computer waste quantities using forecasting techniques. J. Clean. Prod. 2016, 112, 3072–3085. [Google Scholar] [CrossRef] [Green Version]
- Chang, S.-Y.; Assumaning, G.A.; Abdelwahab, Y. Estimation of future generated amount of e-waste in the United States. J. Environ. Prot. 2015, 6, 902. [Google Scholar] [CrossRef] [Green Version]
- Rahmani, M.; Nabizadeh, R.; Yaghmaeian, K.; Mahvi, A.H.; Yunesian, M. Estimation of waste from computers and mobile phones in Iran. Resour. Conserv. Recycl. 2014, 87, 21–29. [Google Scholar] [CrossRef]
- Ikhlayel, M. Differences of methods to estimate generation of waste electrical and electronic equipment for developing countries: Jordan as a case study. Resour. Conserv. Recycl. 2016, 108, 134–139. [Google Scholar] [CrossRef]
- Polák, M.; Drápalová, L. Estimation of end of life mobile phones generation: The case study of the Czech Republic. Waste Manag. 2012, 32, 1583–1591. [Google Scholar] [CrossRef]
- Alavi, N.; Shirmardi, M.; Babaei, A.; Takdastan, A.; Bagheri, N. Waste electrical and electronic equipment (weee) estimation: A case study of Ahvaz City, Iran. J. Air Waste Manag. Assoc. 2015, 65, 298–305. [Google Scholar] [CrossRef] [Green Version]
- Nguyen, D.-Q.; Yamasue, E.; Okumura, H.; Ishihara, K.N. Use and disposal of large home electronic appliances in Vietnam. J. Mater. Cycles Waste Manag. 2009, 11, 358. [Google Scholar] [CrossRef]
- Kim, S.; Oguchi, M.; Yoshida, A.; Terazono, A. Estimating the amount of weee generated in south korea by using the population balance model. Waste Manag. 2013, 33, 474–483. [Google Scholar] [CrossRef]
- Islam, M.T.; Huda, N. E-waste in Australia: Generation estimation and untapped material recovery and revenue potential. J. Clean. Prod. 2019, 237, 117787. [Google Scholar] [CrossRef]
- Gusukuma, M.; Kahhat, R. Electronic waste after a digital tv transition: Material flows and stocks. Resour. Conserv. Recycl. 2018, 138, 142–150. [Google Scholar] [CrossRef]
- Lau, W.K.-Y.; Chung, S.-S.; Zhang, C. A material flow analysis on current electrical and electronic waste disposal from Hong Kong households. Waste Manag. 2013, 33, 714–721. [Google Scholar] [CrossRef] [PubMed]
- Abbondanza, M.; Souza, R. Estimating the generation of household e-waste in municipalities using primary data from surveys: A case study of Sao Jose dos Campos, Brazil. Waste Manag. 2019, 85, 374–384. [Google Scholar] [CrossRef] [PubMed]
- Demirel, E.; Demirel, N.; Gökçen, H. A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. J. Clean. Prod. 2016, 112 Part 3, 2101–2113. [Google Scholar] [CrossRef]
- Özceylan, E.; Paksoy, T. Reverse supply chain optimisation with disassembly line balancing. Int. J. Prod. Res. 2013, 51, 5985–6001. [Google Scholar] [CrossRef]
- Jalili Ghazi Zade, M.; Noori, R. Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. Int. J. Environ. Res. 2008, 2, 13–22. [Google Scholar]
- Kara, S.S.; Onut, S. A stochastic optimization approach for paper recycling reverse logistics network design under uncertainty. Int. J. Environ. Sci. Technol. 2010, 7, 717–730. [Google Scholar] [CrossRef] [Green Version]
- Barros, A.I.; Dekker, R.; Scholten, V. A two-level network for recycling sand: A case study. Eur. J. Oper. Res. 1998, 110, 199–214. [Google Scholar] [CrossRef]
- Fleischmann, M.; Beullens, P.; Bloemhof-Ruwaard, J.M.; Wassenhove, L.N. The impact of product recovery on logistics network design. Prod. Oper. Manag. 2001, 10, 156–173. [Google Scholar] [CrossRef]
- Pati, R.K.; Vrat, P.; Kumar, P. A goal programming model for paper recycling system. Omega 2008, 36, 405–417. [Google Scholar] [CrossRef]
- Nakatani, J.; Konno, K.; Moriguchi, Y. Variability-based optimal design for robust plastic recycling systems. Resour. Conserv. Recycl. 2017, 116, 53–60. [Google Scholar] [CrossRef]
- Phuc, P.N.K.; Yu, V.F.; Tsao, Y.-C. Optimizing fuzzy reverse supply chain for end-of-life vehicles. Comput. Ind. Eng. 2016, 113, 757–765. [Google Scholar] [CrossRef]
- Louwers, D.; Kip, B.J.; Peters, E.; Souren, F.; Flapper, S.D.P. A facility location allocation model for reusing carpet materials. Comput. Ind. Eng. 1999, 36, 855–869. [Google Scholar] [CrossRef]
- Kilic, H.S.; Cebeci, U.; Ayhan, M.B. Reverse logistics system design for the waste of electrical and electronic equipment (weee) in turkey. Resour. Conserv. Recycl. 2015, 95, 120–132. [Google Scholar] [CrossRef]
- John, S.T.; Sridharan, R.; Ram Kumar, P.N.; Krishnamoorthy, M. Multi-period reverse logistics network design for used refrigerators. Appl. Math. Model. 2018, 54, 311–331. [Google Scholar] [CrossRef]
- Jayaraman, V.; Patterson, R.A.; Rolland, E. The design of reverse distribution networks: Models and solution procedures. Eur. J. Oper. Res. 2003, 150, 128–149. [Google Scholar] [CrossRef]
- Min, H.; Jeung Ko, H.; Seong Ko, C. A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega 2006, 34, 56–69. [Google Scholar] [CrossRef]
- Cruz-Rivera, R.; Ertel, J. Reverse logistics network design for the collection of end-of-life vehicles in Mexico. Eur. J. Oper. Res. 2009, 196, 930–939. [Google Scholar] [CrossRef]
- Gomes, M.I.; Barbosa-Povoa, A.P.; Novais, A.Q. Modelling a recovery network for weee: A case study in portugal. Waste Manag. 2011, 31, 1645–1660. [Google Scholar] [CrossRef]
- Diabat, A.; Kannan, D.; Kaliyan, M.; Svetinovic, D. An optimization model for product returns using genetic algorithms and artificial immune system. Resour. Conserv. Recycl. 2013, 74, 156–169. [Google Scholar] [CrossRef]
- Roghanian, E.; Pazhoheshfar, P. An optimization model for reverse logistics network under stochastic environment by using genetic algorithm. J. Manuf. Syst. 2014, 33, 348–356. [Google Scholar] [CrossRef]
- Linh, T.T.D.; Yousef, A.; Sang- Heon, L.; Phan, N.K.P. Optimizing an e-waste reverse supply chain model while incorporating risk costs. Am. J. Eng. Appl. Sci. 2017, 10, 949–958. [Google Scholar]
- Trochu, J.; Chaabane, A.; Ouhimmou, M. Reverse logistics network redesign under uncertainty for wood waste in the crd industry. Resour. Conserv. Recycl. 2018, 128, 32–47. [Google Scholar] [CrossRef]
- Lieckens, K.; Vandaele, N. Multi-level reverse logistics network design under uncertainty. Int. J. Prod. Res. 2012, 50, 23–40. [Google Scholar] [CrossRef] [Green Version]
- Pochampally, K.K.; Gupta, S.M. A multiphase fuzzy logic approach to strategic planning of a reverse supply chain network. IEEE Trans. Electron. Packag. Manuf. 2008, 31, 72–82. [Google Scholar] [CrossRef] [Green Version]
- John, S.T.; Sridharan, R.; Kumar, P.R. Multi-period reverse logistics network design with emission cost. Int. J. Logist. Manag. 2017, 28, 127–149. [Google Scholar] [CrossRef]
- Alumur, S.A.; Nickel, S.; Saldanha-da-Gama, F.; Verter, V. Multi-period reverse logistics network design. Eur. J. Oper. Res. 2012, 220, 67–78. [Google Scholar] [CrossRef]
- Krikke, H.R.; van Harten, A.; Schuur, P.C. Business case océ: Reverse logistic network re-design for copiers. Or-Spektrum 1999, 21, 381–409. [Google Scholar] [CrossRef]
- Shih, L.-H. Reverse logistics system planning for recycling electrical appliances and computers in Taiwan. Resour. Conserv. Recycl. 2001, 32, 55–72. [Google Scholar] [CrossRef]
- Deng, C.-L.; Shao, C.-M. Multi-product min-cost recycling network flow problem. In Global Perspective for Competitive Enterprise, Economy and Ecology; Springer: London, UK, 2009; pp. 653–661. [Google Scholar]
- Fleishmann, M. Quantitative Models for Reverse Logistics. Ph.D. Thesis, Erasmus University Rotterdam, Rotterdam, The Netherlands, 2000. [Google Scholar]
- Lee, D.-H.; Dong, M. Dynamic network design for reverse logistics operations under uncertainty. Transp. Res. Part E Logist. Transp. Rev. 2009, 45, 61–71. [Google Scholar] [CrossRef]
- Ayvaz, B.; Bolat, B.; Aydın, N. Stochastic reverse logistics network design for waste of electrical and electronic equipment. Resour. Conserv. Recycl. 2015, 104, 391–404. [Google Scholar] [CrossRef]
- Demirel, N.Ö.; Gökçen, H. A mixed integer programming model for remanufacturing in reverse logistics environment. Int. J. Adv. Manuf. Technol. 2008, 39, 1197–1206. [Google Scholar] [CrossRef]
- Srivastava, S.K. Network design for reverse logistics. Omega 2008, 36, 535–548. [Google Scholar] [CrossRef]
- Xianfeng, L.; Jianwei, Q.; Meilian, L. Design and simulation weee reverse logistics network in Guangxi. In Proceedings of the 2010 International Conference on Optoelectronics and Image Processing, Haikou, China, 11–12 November 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 403–408. [Google Scholar]
- Grunow, M.; Gobbi, C. Designing the reverse network for weee in Denmark. CIRP Ann. 2009, 58, 391–394. [Google Scholar] [CrossRef]
- Zhi, G.-J.; Dong, X.-B.; Zhang, R.-X. Application of genetic algorithms for the design of weee logistics network model. In Proceedings of the 2010 International Conference on Intelligent Computing and Integrated Systems, Guilin, China, 22–24 October 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 49–52. [Google Scholar]
- Achillas, C.; Vlachokostas, C.; Aidonis, D.; Moussiopoulos, Ν.; Iakovou, E.; Banias, G. Optimising reverse logistics network to support policy-making in the case of electrical and electronic equipment. Waste Manag. 2010, 30, 2592–2600. [Google Scholar] [CrossRef] [PubMed]
- Alshamsi, A.; Diabat, A. A reverse logistics network design. J. Manuf. Syst. 2015, 37 Part 3, 589–598. [Google Scholar] [CrossRef]
- John, S.; Sridharan, R.; Ram Kumar, P. Reverse logistics network design: A case of mobile phones and digital cameras. Int. J. Adv. Manuf. Technol. 2018, 94, 615–631. [Google Scholar] [CrossRef]
- Banguera, L.A.; Sepúlveda, J.M.; Ternero, R.; Vargas, M.; Vásquez, Ó.C. Reverse logistics network design under extended producer responsibility: The case of out-of-use tires in the gran santiago city of chile. Int. J. Prod. Econ. 2018, 205, 193–200. [Google Scholar] [CrossRef]
- Tosarkani, B.M.; Amin, S.H. A multi-objective model to configure an electronic reverse logistics network and third party selection. J. Clean. Prod. 2018, 198, 662–682. [Google Scholar] [CrossRef]
- Messmann, L.; Helbig, C.; Thorenz, A.; Tuma, A. Economic and environmental benefits of recovery networks for weee in europe. J. Clean. Prod. 2019, 222, 655–668. [Google Scholar] [CrossRef]
- Sheu, J.-B. A coordinated reverse logistics system for regional management of multi-source hazardous wastes. Comput. Oper. Res. 2007, 34, 1442–1462. [Google Scholar] [CrossRef]
- Fabiano, B.; Currò, F.; Palazzi, E.; Pastorino, R. A framework for risk assessment and decision-making strategies in dangerous good transportation. J. Hazard. Mater. 2002, 93, 1–15. [Google Scholar] [CrossRef]
- Ho, L.T.; Lin, G.; Nagalingam, S. A risk mitigation framework for integrated-enterprise systems implementation for the manufacturing environment. Int. J. Bus. Inf. Syst. 2009, 4, 290–310. [Google Scholar] [CrossRef]
- Wilson, M.C. The impact of transportation disruptions on supply chain performance. Transp. Res. Part E: Logist. Transp. Rev. 2007, 43, 295–320. [Google Scholar] [CrossRef]
- Özceylan, E.; Paksoy, T. Fuzzy multi-objective linear programming approach for optimising a closed-loop supply chain network. Int. J. Prod. Res. 2013, 51, 2443–2461. [Google Scholar] [CrossRef]
- Jindal, A.; Sangwan, K.S. Closed loop supply chain network design and optimisation using fuzzy mixed integer linear programming model. Int. J. Prod. Res. 2014, 52, 4156–4173. [Google Scholar] [CrossRef]
- Kara, S.S.; Onut, S. A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling. Expert Syst. Appl. 2010, 37, 6129–6137. [Google Scholar] [CrossRef]
- Listeş, O.; Dekker, R. A stochastic approach to a case study for product recovery network design. Eur. J. Oper. Res. 2005, 160, 268–287. [Google Scholar] [CrossRef]
- Pishvaee, M.S.; Torabi, S.A. A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst. 2010, 161, 2668–2683. [Google Scholar] [CrossRef]
- Dubois, D.; Fargier, H.; Fortemps, P. Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. Eur. J. Oper. Res. 2003, 147, 231–252. [Google Scholar] [CrossRef]
- Pishvaee, M.S.; Razmi, J.; Torabi, S.A. Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets Syst. 2012, 206, 1–20. [Google Scholar] [CrossRef]
- Amin, S.H.; Zhang, G. Closed-loop supply chain network configuration by a multi-objective mathematical model. Int. J. Bus. Perform. Supply Chain Model. 2014, 6, 1–15. [Google Scholar] [CrossRef]
- Bilgen, B. Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Syst. Appl. 2010, 37, 4488–4495. [Google Scholar] [CrossRef]
No. | Category | Examples | Amount (Million Tonnes) |
---|---|---|---|
1 | Large devices | Washing machines, large printing machines, dishwashing machines | 11.8 |
2 | Small devices | Vacuum equipment, radio, toasters, toys, medical devices, video cameras, electronic tools | 12.8 |
3 | Small IT and telecommunications equipment | Telephones, printers, personal computers, pocket calculators | 3.0 |
4 | Temperature exchange equipment | Air conditioners, freezers, refrigerators, heat pump | 7.0 |
5 | Lamps | LED lamps, fluorescent lamps, high intensity discharge lamps | 1.0 |
6 | Screen and monitors | Televisions, monitors, laptops | 6.3 |
Forward Supply Chain | Reverse Supply Chain |
---|---|
Focus on increasing profit and cost minimization | Focus on environmental issues, regulations, profit and cost minimization |
Product demand is quite straightforward to forecast | Returned products are relatively difficult to estimate |
The quantity of product is less variation | The quantity of returned products are highly uncertain |
Conventional marketing techniques can be used | There are some elements requiring complicated marketing |
Processing times and stages are well identified | Processing times and stages are vary based on the quality of returned products |
Products are delivered from one location to other locations | Used products are collected from a lot of locations and then reach to one processing centre |
Speed is one of the main factors in terms of competitive advantage | Speed is not an important element |
Product packaging is standard | Returned product packaging highly varies or lack of packaging |
Product structure is standard | Returned product structure is modified |
Cost estimation is quite easier because of accounting systems | Cost factors are complicated to determine |
Disposition options are rather clear | Disposition alternatives depended on the condition of a returned product |
Inventory management is consistent | Inventory management is chaotic |
Cost implications are quite clear | Cost implications are unclear |
Processes for real-time product tracking are highly visible | Processes for returned product tracking are less visible because of lack of information system infrastructure |
Product life cycle changes are easily managed | Product life cycle changes are difficultly managed |
Models are relatively deterministic | Models are more stochastic |
Key importance to manufacturers | Key importance to end-of-life processors (such as remanufacturers, recyclers) |
Factors | Products or Sector | Country | References |
---|---|---|---|
Internal: strategic consideration, financial issues, management skills and technological issues External: public awareness, environmental regulations, economic evaluation, systems and collaboration | Electronic industry | China | Hung Lau and Wang [22] |
Economic elements, regulations, corporate citizenship and environmental and green issues | End-of-life computers | India | Ravi, Shankar and Tiwari [37] |
Financial needs, environmental requirements, social factors | Electronic industry | Taiwan | Chiou, Chen, Yu and Yeh [38] |
Strategic consideration, environmental awareness, economic concerns and social awareness | Electronic waste | Brazil | Guarnieri, e Silva and Levino [39] |
Regulations, the demand of customers, strategic cost, environmental issues, the quantity and quality, incentives and integration and coordination | End-of-life computers | Australia | Rahman and Subramanian [40] |
The incentive dependency of residents, e-waste concern, residents‘ awareness | Electronic waste | Iran | Jafari, Heydari and Keramati [41] |
Top management concern, strategic partners, the relative cost and performance, the strategy of reducing returned products, revenue from recyclable materials, the speed to put products back to markets | Electronic waste | European nations | Janse, Schuur and de Brito [42] |
Consumer behaviour in returning used products | Mobile phone | India | Dixit and Badgaiyan [43] |
Behaviour, education background, community knowledge, attitudes | Electronic waste | Indonesia | Pandebesie, Indrihastuti, Wilujeng and Warmadewanthi [44] |
Subjective norm, environmental knowledge and consumer perception | E-waste | Nigeria | Nduneseokwu et al. [45] |
Public awareness, enterprise perspective, government policy | E-waste | China | Cao et al. [46] |
Laws and regulations, environmental awareness, public media and educational campaigns | E-waste | Vietnam | Thi Thu Nguyen et al. [47] |
Reference | Main Goal | Modelling Approach/Method | Dimension Concerns | Country |
---|---|---|---|---|
Cucchiella, D’Adamo, Koh and Rosa [15] | Evaluating recovery value of 14 types of e-waste from the recycling process | Literature analysis | Economic | European |
Keh, Rodhain, Meissonier and Llorca [48] | Evaluating the integrated RSC model | IBM case study | Economic, environmental and social | France |
Lin, Wen and Tsai [49] | Determining the priority for e-waste recycling | AHP | Environmental | Taiwan |
Ravi [50] | Evaluating the quality of recycling operation | Multi Attribute Global Inference of Quality (MAGIQ) (a conceptual model) | Environmental and economic | India |
Bereketli, Erol Genevois, Esra Albayrak and Ozyol [51] | Evaluating and selecting of e-waste treatment strategies | A linear programming technique for multidimensional analysis of preference (LINMAP) | Environmental and economic | Turkey |
Liu, Zhong and Wei [52] | Evaluation of e-waste RL capability | Index system and quantitative approaches | - | China |
Ponce-Cueto, Manteca and Carrasco-Gallego [54] | Determining the locations of collection areas | AHP | Economic | Spain |
Tsai and Hung [55] | Optimizing treatment and recycling systems | Activity-based costing (ABC) | Environmental and economic | Taiwan |
Dhib, Addouche, El Mhamdi and Loukil [56] | A compromising strategy for sustainable performance of e-waste management | Entropic approach, Fuzzy set theory | Environmental, economic, and social | Tunisia |
Duygan and Meylan [58] | Providing policy support for e-waste management | Material Flow Analysis and structural analysis | Social, economic, technical and political aspects | Switzerland |
Jayant et al. [59] | Evaluating the selection of RSC Service Providers | Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method | Environmental and economic | India |
Sahu et al. [60] | Recognizing the important enablers for the responsibility of replacing obsolete mobile phones | Decision-Making Trial and Evaluation Laboratory (The Grey-DEMATEL) | Environmental | India |
Tran et al. [61] | Analysing the management of TVs in urban areas | Material Flow Analysis (MFA) | Environmental | Vietnam |
Bahers and Kim [62] | Evaluating the implementation of Extended Producer Responsibility for e-waste activities | Material Flow Analysis | Environmental, economic elements | France |
Agrawal et al. [63] | Analysing critical strategic issues and challenges faced by a real electronics industry | Interviews, Strength, Weaknesses, Opportunities, and Threats (SWOT) | Environmental, economic aspects | India |
Shokohyar and Mansour [64] | Designing the sustainable recovery network for e-waste | AHP and simulation optimization method | Environmental, economic and social aspects | Iran |
De Meester et al. [65] | Optimizing the environmental performance of the e-waste recycling system assisting decision markers | Material Flow Analysis and life cycle assessment | Environmental | Belgium |
Isernia et al. [66] | Evaluating the e-waste collection system | The probability transition matrix | Environmental, economic and social aspects | Italy |
Govindan et al. [67] | Selecting partners for sustainable reverse supply chain collaboration | Complex Proportional Assessment, the best worst method | Environmental, economic and social aspects | India |
Authors | Model Focus | Method | Variables for Forecasting | Product/Equipment | Country/Region |
---|---|---|---|---|---|
Hanafi, Kara and Kaebernick [69] | Estimating mobile phone returns | Fuzzy Coloured Petri Net | Income distribution, demographics, sales data, the lifespan of the electronic equipment | Obsolete mobile phones | Australia |
Araújo et al. [71] | The quantity of e-waste generated | Time-step, consumption and use | An average lifetime, the number of sales and stock | E-waste | Brazil |
Steubing et al. [72] | Evaluating computer waste generation | Material flow analysis | Data on sales and import | Computer waste | Chile |
Andarani and Goto [73] | Forecasting e-waste from households generated | Material flow analysis | The lifespan of the electronic devices, consumption data, consumer’s behaviour | Televisions, washing machines, refrigerators, personal computers, and mobile phones | Indonesia |
Petridis et al. [74] | Forecasting the quantity of computer waste | Different forecasting approaches including Bass, Gompertz, Logistic, Trend model, Level model, Auto Regressive Moving Average and Exponential Smoothing | Data sales, the lifespan of a computer | Computer waste | Europe, Asia, Australia, Americas |
Chang, Assumaning and Abdelwahab [75] | Predicting the amount of e-waste generation | Decreasing rate, polynomial regression analysis | Sales data, Lifespan of the electronic equipment | E-waste of thirteen selected electronic devices | United States |
Rahmani et al. [76] | Estimating the past and future quantity of end-of-life computers and mobile phones generated | Time-series and logistic function | The lifespan of the electronic equipment | End-of-life computers and mobile phones | Iran |
Ikhlayel [77] | Comparing different methods to estimate e-waste generation | Consumption and use, Simple Delay, Time Step, Mass Balance, Approximation 2 | The number of households, the average number of people per household, current and future population, and the sales rate | E-waste of six selected electronic equipment | Jordan |
Polák and Drápalová [78] | Estimating the quantity of obsolete mobile phone generation | Delay | The lifespan of the electronic equipment, data on the import and export | Mobile phones | Czech Republic |
Alavi et al. [79] | Predicting e-waste generation | Consumption and use | The lifetime of the electronic equipment | E-waste | Iran |
Nguyen et al. [80] | The use of large home applications and the estimation of e-waste generation in future | Weibull distribution, the logistic function, and the population balance model | The lifespan of electronic equipment, the family size, and data sales | Five large home appliances | Vietnam |
Kim et al. [81] | E-waste generation | Population balance model, Weibull distribution | The lifespan of electronic equipment, domestic shipments, the number of electronic products owned per household | Eight selected e-waste | South Korea |
Islam and Huda [82] | Estimating e-waste generation, recoverable materials, potential revenue | Holt’s double exponential smoothing and Weibull distribution and Monte Carlo simulation | Stock estimation, dynamic lifespans, the assessment of put-on-market | E-waste | Australia |
Gusukuma and Kahhat [83] | Estimating the flows and the stocks of CRT Televisions in 2018-2025 | Dynamic Material Flow Analysis | Importers, Household users, Business and Public, Intermediaries | Cathode Ray Tube (CRT) Televisions | Peru |
Lau et al. [84] | Estimating disposal outlets | Material flow analysis | Consumer behaviour, private e-waste trader survey | TVs, washing machine, air conditioners, refrigerator, personal computers | Hong Kong |
Abbondanza and Souza [85] | Estimating e-waste generation in Sao Jose dos Campos city | Survey | E-waste lifespan, disposal profiles, estimated population | Sixteen selected types of e-waste | Brazil |
References | Goal | Network Stages | Model | Uncertain Parameters | ||||||
---|---|---|---|---|---|---|---|---|---|---|
CF | DF/SF | RcF | RpF | DpF | SM | PM | ||||
Doan et al. [1] | CM | √ | √ | √ | √ | √ | √ | √ | FMILP | Quantity, shipping, processing, recycling and risk costs, capacity |
Dat et al. [36] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Kilic, Cebeci and Ayhan [96] | CM | √ | √ | √ | √ | MILP | Not considered | |||
John et al. [97] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Gomes, Barbosa-Povoa and Novais [101] | CM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Linh et al. [104] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Lieckens and Vandaele [106] | PM | √ | √ | √ | √ | √ | MINLP | Transportation delays, and inventory levels | ||
John, Sridharan and Kumar [108] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Alumur et al. [109] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Krikke, van Harten and Schuur [110] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Shih [111] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Deng and Shao [112] | CM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Lee and Dong [114] | CM | √ | √ | √ | SMILP | Quantity of returned products | ||||
Ayvaz, Bolat and Aydın [115] | PM | √ | √ | √ | √ | √ | SMILP | Quantity, quality and shipping costs | ||
Demirel and Gökçen [116] | CM | √ | √ | √ | MILP | Not considered | ||||
Srivastava [117] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Xianfeng et al. [118] | CM | √ | √ | √ | √ | MILP | Not considered | |||
Grunow and Gobbi [119] | CM | √ | √ | √ | √ | √ | MILP | Not considered | ||
Zhi et al. [120] | CM | √ | √ | √ | MILP | Not considered | ||||
Achillas et al. [121] | CM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
Alshamsi and Diabat [122] | PM | √ | √ | √ | √ | √ | √ | MILP | Not considered | |
John et al. [123] | PM | √ | √ | √ | √ | √ | √ | √ | MILP | Not considered |
Banguera et al. [124] | PM | √ | √ | √ | √ | √ | MILP | Not considered | ||
Tosarkani and Amin [125] | PM | √ | √ | √ | √ | √ | FANP, MILP | Not considered | ||
Messmann et al. [126] | CM | √ | √ | √ | √ | √ | MILP | Not considered |
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Doan, L.T.T.; Amer, Y.; Lee, S.-H.; Phuc, P.N.K.; Dat, L.Q. E-Waste Reverse Supply Chain: A Review and Future Perspectives. Appl. Sci. 2019, 9, 5195. https://doi.org/10.3390/app9235195
Doan LTT, Amer Y, Lee S-H, Phuc PNK, Dat LQ. E-Waste Reverse Supply Chain: A Review and Future Perspectives. Applied Sciences. 2019; 9(23):5195. https://doi.org/10.3390/app9235195
Chicago/Turabian StyleDoan, Linh Thi Truc, Yousef Amer, Sang-Heon Lee, Phan Nguyen Ky Phuc, and Luu Quoc Dat. 2019. "E-Waste Reverse Supply Chain: A Review and Future Perspectives" Applied Sciences 9, no. 23: 5195. https://doi.org/10.3390/app9235195
APA StyleDoan, L. T. T., Amer, Y., Lee, S. -H., Phuc, P. N. K., & Dat, L. Q. (2019). E-Waste Reverse Supply Chain: A Review and Future Perspectives. Applied Sciences, 9(23), 5195. https://doi.org/10.3390/app9235195