# E-Waste Reverse Supply Chain: A Review and Future Perspectives
## 4 E-Waste Reverse Supply Chains
### Research on E-Waste Reverse Supply Chain Network Design Models
A research regarding a national recovery network for e-waste in Portugal was conducted by [PERSON], [PERSON] and [PERSON] [101]. MILP model was applied to select suitable locations for collection and sorting centres with total cost minimization. Results from the case study showed that recycling and shipping costs are the key elements in the design of an RSC network. To eliminate the variance from the actual RSC network problem, these costs should be carefully estimated. [PERSON] et al. [109] proposed an RSC network design for multi-products by applying MILP to maximize the profit. Washing machines and tumble dryers in Germany were conducted to validate the model. The research found that to reduce transportation costs, inspection and remanufacturing centres can be co-located. However, this model did not consider uncertain parameters in the model. [PERSON] et al. [36] suggested an e-waste RL model with different treatment options (i.e., reuse, recycling, repair) by using an MILP approach which was modelled by utilizing a mathematical programming language (AMPL) and then solved with CPLEX software. The research can assist decision-makers to select the optimal locations to build centres and the flow of materials in the RL network. [PERSON], [PERSON] and [PERSON] [96] examined an e-waste RL system with 10 different scenarios of collecting rates in Turkey. An MILP model was also applied to minimize the overall cost of the RL system. Compared to the other existing researches, different categories of storage and recycling centres were investigated in the model. The authors suggested that the model can be enhanced by integrating reuse and remanufacture options in further studies. To consider risk elements in RSC models, [PERSON] et al. [104] proposed a new model integrating collection risks, transportation risks and operating risks. The study concluded that the RSC network design has been obviously modified with risk factors considered. The uncertainty of RSC network design is also one of the key problems to be considered [113]. To address uncertain parameters including the quantity, quality, the return rate of used products and related costs in e-waste RSC network design studies, stochastic programming is considered as a common approach [89; 114]. To deal with the uncertainty of demand and return rate in RSC network design model, [PERSON] and [PERSON] [114] developed a two-stage stochastic programming model that was enhanced from a deterministic model. The solution approach incorporated a sample average approximation approach with simulated annealing. The research concluded that compared to the deterministic optimization approach, the stochastic approach could provide a closer true network design problem. [PERSON] et al. [115] also proposed a multi-tiers, multi-products RSC network design for e-waste to optimize the profit maximization by using a stochastic programming model. The research examined the quantity and quality of returned products and transportation costs as uncertainties. A recycling industry for e-waste in Turkey was conducted to demonstrate the possible application of the model. The sample average approximation method was utilized in order to solve the model. The study showed that the model provided appropriate solutions in making efficient decisions regarding the uncertainties of the quantity and quality of used products, and transportation cost. The summary of e-waste RSC network design models can be seen in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Goal**} & \multicolumn{6}{c}{**Network Stages**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Uncertain**} \\  & & **CF** & **DF/SF** & **RCF** & **RpF** & **DpF** & **SM** & **PM** & & \\ \hline \hline \multirow{2}{*}{[PERSON] et al.